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Real

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Table of Links Abstract and Introduction


Domain and Task
2.1. Data sources and complexity
2.2. Task definition


Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review


Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development


Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration


Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data


Conclusion, Acknowledgements, and References 5.2. System demonstration In this section, we present the end system to demonstrate the ‘supplier risk profiles’ in action. First, informed by the evaluation above, we retrained the best-performing model - random forest - using all the labelled datasets for each component in the pipeline. After retraining all models, we apply our workflow to the entire raw TED dataset. This contains roughly 3.3 million healthcare related tender notices (with contract awards) covering 2011 to 2022, involving over 167 thousands unique suppliers, 86 thousands buyers, with higher than $2 trillion in monetary value. Processing this massive dataset using our workflow explained above allowed us to create the biggest healthcare procurement database to date. We then run queries to obtain data from the database to calculate the above-mentioned metrics for each supplier. We show a few examples in screenshots below. Figure 8 shows the supplier risk profile in terms of ‘ability to supply’ and ‘economic risk’ for Bausch & Lomb, based on their contracts won between 2011 and 2022. The line chart on the left shows a number of ‘buyer’ metrics (BM) selected for review, such as: ‘buyer countries’ that measures a supplier’s global reach by considering countries they won contract in; ‘buyers - moving average’ that considers the number of active buyers for a supplier; and ‘buyers - yearly participation’ that considers the number of active buyers for supplier each year. The line chart on the right aggregates these selected metrics to show an overall trend. Figure 9 shows the supplier risk profile (also ‘ability to supply’ and ‘economic risk’) for Siemens covering the same time period, but using a mixture of ‘lot’ and ‘buyer’ metrics (LM and BM). For example, ‘buyer - churn/retention rate’ that measures the change in the supplier’s clients (based on the number of new buyers they had and lost during each time period); ‘lots - average duration days’ and ‘lots - duration days’ looking at lot duration in days to understand lot delivery time frames. Each figure demonstrates risks of a specific supplier from different perspectives, hence allowing users to thoroughly evaluate a supplier. Figure 10 and 11 compare global suppliers in a single view. Generally, a straight line with little fluctuations is desirable as that indicates little change in risks over time. We can notice that in Figure 10, most suppliers selected for review have relatively little change in terms of their risks. This is mainly due to them being large, established suppliers that tend to win a continuous stream of contracts over time. However, some suppliers had more fluctuations in their risk indices compared to others, suggesting they may be riskier choices to buyers. Figure 11 compares several smaller suppliers, and we can see that their risk patterns are much more erratic, due to a lack of continuity in their track record. Authors:
(1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk);
(2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io);
(3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io);
(4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk);
(5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and Introduction Domain and Task
2.1. Data sources and complexity
2.2. Task definition Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data Conclusion, Acknowledgements, and References Abstract and Introduction Abstract and Introduction Abstract and Introduction Abstract and Introduction Domain and Task 2.1. Data sources and complexity 2.2. Task definition Domain and Task Domain and Task 2.1. Data sources and complexity 2.1. Data sources and complexity 2.2. Task definition 2.2. Task definition Related Work 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review Related Work Related Work 3.1. Text mining and NLP research overview 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review 3.4. Conclusion from literature review Proposed Methodology 4.1. Domain knowledge 4.2. Content extraction 4.3. Lot zoning 4.4. Lot item detection 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development Proposed Methodology Proposed Methodology Proposed Methodology 4.1. Domain knowledge 4.1. Domain knowledge 4.2. Content extraction 4.2. Content extraction 4.3. Lot zoning 4.3. Lot zoning 4.4. Lot item detection 4.4. Lot item detection 4.5. Lot parsing 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development 4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration 5.1. Component evaluation 5.2. System demonstration Experiment and Demonstration Experiment and Demonstration 5.1. Component evaluation 5.1. Component evaluation 5.2. System demonstration 5.2. System demonstration Discussion 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.4. The cost of training data Discussion Discussion 6.1. The ‘industry’ focus of the project 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.3. The dilemma of algorithmic choices 6.4. The cost of training data 6.4. The cost of training data Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References 5.2. System demonstration In this section, we present the end system to demonstrate the ‘supplier risk profiles’ in action. First, informed by the evaluation above, we retrained the best-performing model - random forest - using all the labelled datasets for each component in the pipeline. After retraining all models, we apply our workflow to the entire raw TED dataset. This contains roughly 3.3 million healthcare related tender notices (with contract awards) covering 2011 to 2022, involving over 167 thousands unique suppliers, 86 thousands buyers, with higher than $2 trillion in monetary value. Processing this massive dataset using our workflow explained above allowed us to create the biggest healthcare procurement database to date. We then run queries to obtain data from the database to calculate the above-mentioned metrics for each supplier. We show a few examples in screenshots below. Figure 8 shows the supplier risk profile in terms of ‘ability to supply’ and ‘economic risk’ for Bausch & Lomb, based on their contracts won between 2011 and 2022. The line chart on the left shows a number of ‘buyer’ metrics (BM) selected for review, such as: ‘buyer countries’ that measures a supplier’s global reach by considering countries they won contract in; ‘buyers - moving average’ that considers the number of active buyers for a supplier; and ‘buyers - yearly participation’ that considers the number of active buyers for supplier each year. The line chart on the right aggregates these selected metrics to show an overall trend. Figure 9 shows the supplier risk profile (also ‘ability to supply’ and ‘economic risk’) for Siemens covering the same time period, but using a mixture of ‘lot’ and ‘buyer’ metrics (LM and BM). For example, ‘buyer - churn/retention rate’ that measures the change in the supplier’s clients (based on the number of new buyers they had and lost during each time period); ‘lots - average duration days’ and ‘lots - duration days’ looking at lot duration in days to understand lot delivery time frames. Each figure demonstrates risks of a specific supplier from different perspectives, hence allowing users to thoroughly evaluate a supplier. Figure 10 and 11 compare global suppliers in a single view. Generally, a straight line with little fluctuations is desirable as that indicates little change in risks over time. We can notice that in Figure 10, most suppliers selected for review have relatively little change in terms of their risks. This is mainly due to them being large, established suppliers that tend to win a continuous stream of contracts over time. However, some suppliers had more fluctuations in their risk indices compared to others, suggesting they may be riskier choices to buyers. Figure 11 compares several smaller suppliers, and we can see that their risk patterns are much more erratic, due to a lack of continuity in their track record. Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). Authors: Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

Demonstrating Supplier Risk Profiles with Real-World Data

Table of Links Abstract and 1. Introduction 2. Preliminaries and 2.1. Blind deconvolution 2.2. Quadratic neural networks 3. Methodology 3.1. Time domain quadratic convolutional filter 3.2. Superiority of cyclic features extraction by QCNN 3.3. Frequency domain linear filter with envelope spectrum objective function 3.4. Integral optimization with uncertainty-aware weighing scheme 4. Computational experiments 4.1. Experimental configurations 4.2. Case study 1: PU dataset 4.3. Case study 2: JNU dataset 4.4. Case study 3: HIT dataset 5. Computational experiments 5.1. Comparison of BD methods 5.2. Classification results on various noise conditions 5.3. Employing ClassBD to deep learning classifiers 5.4. Employing ClassBD to machine learning classifiers 5.5. Feature extraction ability of quadratic and conventional networks 5.6. Comparison of ClassBD filters 6. Conclusions Appendix and References 4.2. Case study 1: PU dataset 4.2.1. Dataset description The PU dataset was collected by Paderborn University (PU) Bearing Data Center [79]. This dataset encompasses multiple faults for 32 bearings, including vibration and current signals. The bearings were categorized into three groups: i) Six healthy bearings; ii) Twelve bearings with manually-induced damage (seven with outer race faults, five with inner race faults); iii) Fourteen bearings with real damage, induced by accelerated lifetime tests (five with outer race faults, six with inner race faults, and three with multiple faults). Four operating conditions were implemented, varying the rotational speed (N = 1500rpm or N = 900rpm), load torque (M = 0.7Nm or M = 0.1Nm), and radial force (F = 1000N or F = 400N). The sampling frequency was set at 64KHz. This dataset is one of the more difficult datasets in the field of bearing fault diagnosis [71]. In this study, we exclusively utilize the real damaged bearings for classification to validate the methods in real-world scenarios. We classify the fourteen faulty bearings and six healthy bearings into fourteen categories, as illustrated in Table 3. All the four operating conditions are tested in the experiments, with the codes assigned as N09M07F10, N15M01F10, N15M07F4, and N15M07F10. Given the large volume of data in the PU dataset, each bearing has 20 segments, so we do not use overlapping to construct the datasets. We slice 20 × 2048 sub-segments with a stride of 2048, resulting in 400 data per bearing. The total number of datasets is 7600 × 2048. The data collected in the first 19 segments are randomly divided into training and validation sets at a ratio of 0.8:0.2, while the data from the 20th segment are allocated to the test set. Consequently, the dataset includes 5776 training data, 1444 validation data, and 380 test data. Lastly, we established four SNR levels (-4dB, -2dB, 0dB, 2dB) to evaluate the diagnosis performance. 4.2.2. Classification results The classification results are presented in Table 4. We highlight key findings from the analysis. Notably, the ClassBD model demonstrates superior performance compared to its competitors across various noise levels and operating conditions. Overall, the average F1 scores of ClassBD on the four conditions are higher than 94%. Specifically, under high noise conditions (at -4 dB), the ClassBD exhibits a substantial performance gap relative to other methods. For instance, on the N15M01F10 dataset with -4dB noise, the ClassBD achieves an impressive 95% F1 score, while the second-best method, EWSNet, only attains 70% F1. These results underscore the efficacy of ClassBD as a robust anti-noise model, consistently delivering competitive performance across diverse high-noise scenarios. 4.2.3. Classification under small sample conditions In this study, we rigorously assess the performance of various methods under the challenging scenario of extremely limited sample availability. While our approach is not explicitly tailored for small sample issues, we conduct straightforward tests to gain insights into their behavior. Specifically, we employ the N09M07F10 dataset, comprising 20 signal segments per category. In the most stringent case, we retain only one sample from each signal segment, reserving the last segment of the signal as the test set. Consequently, we collect one to three samples from each signal segment and finally compose 15, 30, and 45 samples per class for training sets. Notably, we exclude noise from this experiment. The results, depicted in Figure 3, reveal that even under these constrained conditions, the ClassBD, EWSNet, and DRSN models exhibit commendable performance. Remarkably, all three methods achieve over 90% F1 scores with a training dataset of just 15 samples per class. Notably, the performance of ClassBD and EWSNet closely align. Their average F1 scores stand at 97.70% and 97.47%, respectively, positioning them as the top-performing approaches. In summary, our findings underscore the promising potential of ClassBD in addressing the challenges posed by small sample sizes Authors:
(1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China;
(3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China;
(4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Preliminaries and 2.1. Blind deconvolution 2. Preliminaries and 2.1. Blind deconvolution 2.2. Quadratic neural networks 2.2. Quadratic neural networks 3. Methodology 3. Methodology 3.1. Time domain quadratic convolutional filter 3.1. Time domain quadratic convolutional filter 3.2. Superiority of cyclic features extraction by QCNN 3.2. Superiority of cyclic features extraction by QCNN 3.3. Frequency domain linear filter with envelope spectrum objective function 3.3. Frequency domain linear filter with envelope spectrum objective function 3.4. Integral optimization with uncertainty-aware weighing scheme 3.4. Integral optimization with uncertainty-aware weighing scheme 4. Computational experiments 4.1. Experimental configurations 4.1. Experimental configurations 4.2. Case study 1: PU dataset 4.2. Case study 1: PU dataset 4.3. Case study 2: JNU dataset 4.3. Case study 2: JNU dataset 4.4. Case study 3: HIT dataset 4.4. Case study 3: HIT dataset 5. Computational experiments 5.1. Comparison of BD methods 5.1. Comparison of BD methods 5.2. Classification results on various noise conditions 5.2. Classification results on various noise conditions 5.3. Employing ClassBD to deep learning classifiers 5.3. Employing ClassBD to deep learning classifiers 5.4. Employing ClassBD to machine learning classifiers 5.4. Employing ClassBD to machine learning classifiers 5.5. Feature extraction ability of quadratic and conventional networks 5.5. Feature extraction ability of quadratic and conventional networks 5.6. Comparison of ClassBD filters 5.6. Comparison of ClassBD filters 6. Conclusions 6. Conclusions Appendix and References Appendix and References 4.2. Case study 1: PU dataset 4.2.1. Dataset description 4.2.1. Dataset description 4.2.1. Dataset description The PU dataset was collected by Paderborn University (PU) Bearing Data Center [79]. This dataset encompasses multiple faults for 32 bearings, including vibration and current signals. The bearings were categorized into three groups: i) Six healthy bearings; ii) Twelve bearings with manually-induced damage (seven with outer race faults, five with inner race faults); iii) Fourteen bearings with real damage, induced by accelerated lifetime tests (five with outer race faults, six with inner race faults, and three with multiple faults). Four operating conditions were implemented, varying the rotational speed (N = 1500rpm or N = 900rpm), load torque (M = 0.7Nm or M = 0.1Nm), and radial force (F = 1000N or F = 400N). The sampling frequency was set at 64KHz. This dataset is one of the more difficult datasets in the field of bearing fault diagnosis [71]. In this study, we exclusively utilize the real damaged bearings for classification to validate the methods in real-world scenarios. We classify the fourteen faulty bearings and six healthy bearings into fourteen categories, as illustrated in Table 3. All the four operating conditions are tested in the experiments, with the codes assigned as N09M07F10, N15M01F10, N15M07F4, and N15M07F10. Given the large volume of data in the PU dataset, each bearing has 20 segments, so we do not use overlapping to construct the datasets. We slice 20 × 2048 sub-segments with a stride of 2048, resulting in 400 data per bearing. The total number of datasets is 7600 × 2048. The data collected in the first 19 segments are randomly divided into training and validation sets at a ratio of 0.8:0.2, while the data from the 20th segment are allocated to the test set. Consequently, the dataset includes 5776 training data, 1444 validation data, and 380 test data. Lastly, we established four SNR levels (-4dB, -2dB, 0dB, 2dB) to evaluate the diagnosis performance. 4.2.2. Classification results 4.2.2. Classification results 4.2.2. Classification results The classification results are presented in Table 4. We highlight key findings from the analysis. Notably, the ClassBD model demonstrates superior performance compared to its competitors across various noise levels and operating conditions. Overall, the average F1 scores of ClassBD on the four conditions are higher than 94%. Specifically, under high noise conditions (at -4 dB), the ClassBD exhibits a substantial performance gap relative to other methods. For instance, on the N15M01F10 dataset with -4dB noise, the ClassBD achieves an impressive 95% F1 score, while the second-best method, EWSNet, only attains 70% F1. These results underscore the efficacy of ClassBD as a robust anti-noise model, consistently delivering competitive performance across diverse high-noise scenarios. 4.2.3. Classification under small sample conditions 4.2.3. Classification under small sample conditions 4.2.3. Classification under small sample conditions In this study, we rigorously assess the performance of various methods under the challenging scenario of extremely limited sample availability. While our approach is not explicitly tailored for small sample issues, we conduct straightforward tests to gain insights into their behavior. Specifically, we employ the N09M07F10 dataset, comprising 20 signal segments per category. In the most stringent case, we retain only one sample from each signal segment, reserving the last segment of the signal as the test set. Consequently, we collect one to three samples from each signal segment and finally compose 15, 30, and 45 samples per class for training sets. Notably, we exclude noise from this experiment. The results, depicted in Figure 3, reveal that even under these constrained conditions, the ClassBD, EWSNet, and DRSN models exhibit commendable performance. Remarkably, all three methods achieve over 90% F1 scores with a training dataset of just 15 samples per class. Notably, the performance of ClassBD and EWSNet closely align. Their average F1 scores stand at 97.70% and 97.47%, respectively, positioning them as the top-performing approaches. In summary, our findings underscore the promising potential of ClassBD in addressing the challenges posed by small sample sizes Authors: (1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China; (3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; (4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China. Authors: Authors: (1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China; (3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; (4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; (6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv

ClassBD Outperforms Competitors in Real-World Bearing Fault Diagnosis Using PU Dataset

Table of Links Abstract and Introduction


Domain and Task
2.1. Data sources and complexity
2.2. Task definition


Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review


Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development


Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration


Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data


Conclusion, Acknowledgements, and References 3.4. Conclusion from literature review Our literature review shows that, despite significant research in the areas of text mining and NLP, there is a strong dominance by supervised methods built on well-curated data that do not transfer well to practical scenarios. This is partially reflected by the number of industrial text mining/NLP studies that incorporated rule-based methods and the use of domain lexicons, except a few areas (e.g., the legal domain) where high quality curated resources are abundant. The majority of industrial studies also look at single and sometimes simplified tasks, but do not report a full process in an end-to-end fashion, particularly with a lack of details on how data heterogeneity and inconsistency is dealt with by their methods. Further, no prior work has focused on the healthcare domain. Our work will address these gaps. Authors:
(1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk);
(2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io);
(3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io);
(4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk);
(5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and Introduction Domain and Task
2.1. Data sources and complexity
2.2. Task definition Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data Conclusion, Acknowledgements, and References Abstract and Introduction Abstract and Introduction Abstract and Introduction Abstract and Introduction Domain and Task 2.1. Data sources and complexity 2.2. Task definition Domain and Task Domain and Task 2.1. Data sources and complexity 2.1. Data sources and complexity 2.2. Task definition 2.2. Task definition Related Work 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review Related Work Related Work 3.1. Text mining and NLP research overview 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review 3.4. Conclusion from literature review Proposed Methodology 4.1. Domain knowledge 4.2. Content extraction 4.3. Lot zoning 4.4. Lot item detection 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development Proposed Methodology Proposed Methodology Proposed Methodology 4.1. Domain knowledge 4.1. Domain knowledge 4.2. Content extraction 4.2. Content extraction 4.3. Lot zoning 4.3. Lot zoning 4.4. Lot item detection 4.4. Lot item detection 4.5. Lot parsing 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development 4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration 5.1. Component evaluation 5.2. System demonstration Experiment and Demonstration Experiment and Demonstration 5.1. Component evaluation 5.1. Component evaluation 5.2. System demonstration 5.2. System demonstration Discussion 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.4. The cost of training data Discussion Discussion 6.1. The ‘industry’ focus of the project 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.3. The dilemma of algorithmic choices 6.4. The cost of training data 6.4. The cost of training data Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References 3.4. Conclusion from literature review Our literature review shows that, despite significant research in the areas of text mining and NLP, there is a strong dominance by supervised methods built on well-curated data that do not transfer well to practical scenarios. This is partially reflected by the number of industrial text mining/NLP studies that incorporated rule-based methods and the use of domain lexicons, except a few areas (e.g., the legal domain) where high quality curated resources are abundant. The majority of industrial studies also look at single and sometimes simplified tasks, but do not report a full process in an end-to-end fashion, particularly with a lack of details on how data heterogeneity and inconsistency is dealt with by their methods. Further, no prior work has focused on the healthcare domain. Our work will address these gaps. Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). Authors: Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

Why Supervised Methods in NLP Struggle in Real-World Applications

AI hug videos are taking social media by storm with many content creators relying on them to help boost engagement online. After all, these videos can be very heartwarming for audiences to watch. For this reason, finding the right AI hug video generator is extremely important. Pollo AI is a popular choice among online users for its simplicity and effectiveness in generating quality AI hug videos. But then, how does it work? In this detailed guide, we’ll explore everything about AI hug videos including what makes Pollo AI special! So, let’s get right into it! What Are AI Hug Videos Used For? AI hug videos are mostly used as a form of entertainment but they can also be useful for anyone looking to take their social media game to the next level. In this respect, Pollo AI is a powerful AI hug video generator that can help you create custom virtual hugs using basic character images, be they real or even cartoon subjects. In other words, you can make your closest family members, friends, celebrities, or even your favorite comic-book characters hug it out with just a few clicks. This also means you can tailor these videos to cater to a wide range of use cases such as marketing campaigns, personal messages, family moments, and much more. How Does Pollo AI's AI Hug Video Generator Work? Pollo AI is the leading AI video generator currently on the market. Its AI hugging video generator uses highly sophisticated algorithms that can analyze your static character images and their positions to create realistic AI-hugging animations. This enables you to consistently produce heartwarming AI hug videos that can effectively keep viewers engaged. How To Create AI Hugging Videos Online for Free? Pollo AI is designed to be extremely user-friendly, which means you can generate AI hug videos in just a few clicks. You don’t even need any video editing experience to get started. Step 1: Head to the Pollo AI's AI hugging video generator. Next up, you will need to prepare two high-quality images with two characters. You will also need to set their aspect ratio, in terms of length and width. Step 2: Write a detailed prompt to describe how you want the AI hug video to appear. Try to be as detailed as possible by mentioning background, character behavior, etc. Alternatively, you can use the default prompt provided by the platform. Step 3: Click on ‘Create’ to start the generation process. Normally it takes 10 credits to generate the AI hugging video. Within minutes, the tool will generate an AI hug video. You can also find the final output in the ‘My Creations’ section. Is Pollo AI The Best Way To Create AI Hug Videos? With this AI hug video generator, you can create quality videos that boost audience engagement. This is because the tool does well to analyze your detailed prompts and produce tailored AI hug videos that effectively mirror your desired requirements. This makes it easier to create personalized videos that speak well to audience interests. Aside from that, creating quality AI hug videos can be a time-consuming process but with Pollo AI, it takes only a matter of minutes to get the video output you seek. Moreover, the platform is fast and efficient, so you can consistently generate multiple videos to accommodate any bulk video content needs. On top of that, Pollo AI is a versatile tool that caters to any potential use case. You can use it to create AI hug videos with real people or even animated characters and it will consistently deliver realistic and memorable output. Suffice it to say that Pollo AI can be relied on to create AI hug videos that will keep people entertained. What Other Unique Features Does Pollo AI Offer? There are several unique features that Pollo AI stands out for in video creation. So, let’s quickly break down some of its key capabilities in detail below. Image To Video With Pollo AI, you can instantly transform static images into high-resolution videos. It lets you convert real-life or even animated photos to create moving characters with realistic expressions and motions in just a few clicks. Text To Video You can use simple text prompts to create all sorts of engaging videos. All you need to do is describe what you want to see, eg. characters, backgrounds, actions, etc. The more detailed the prompt, the more accurate the final video output will be. Video To Video You can use Pollo AI to recreate any existing videos into clay, pencil, pop, pixel, anime, or even Disney Pixar-style animations. All it takes is a few clicks to reimagine any real-life footage, making it more dramatic and stylistic enough to captivate audiences. AI Video Templates Pollo AI has numerous AI video templates that can help you create fun and memorable videos. For instance, in addition to AI hugging videos, you can also create AI kissing and handshake videos you can access AI Squish, AI Inflate, AI Explode, and even AI Melt effects to transform images into entertaining and attention-grabbing videos within minutes. Conclusion AI hug videos can be a great way to garner emotion and relatability with audiences online. Whether it’s creating memorable family videos or entertaining animated videos, with the right tool you can produce emotional and believable AI hugging scenes. As we’ve established, Pollo AI can produce quality results with little to no delay. All you need is to follow the steps above and you can craft stunning AI-hugging videos without any hassle. AI hug videos are taking social media by storm with many content creators relying on them to help boost engagement online. After all, these videos can be very heartwarming for audiences to watch. For this reason, finding the right AI hug video generator is extremely important. Pollo AI is a popular choice among online users for its simplicity and effectiveness in generating quality AI hug videos. But then, how does it work? In this detailed guide, we’ll explore everything about AI hug videos including what makes Pollo AI special! So, let’s get right into it! Pollo AI Pollo AI What Are AI Hug Videos Used For? AI hug videos are mostly used as a form of entertainment but they can also be useful for anyone looking to take their social media game to the next level. In this respect, Pollo AI is a powerful AI hug video generator that can help you create custom virtual hugs using basic character images, be they real or even cartoon subjects. In other words, you can make your closest family members, friends, celebrities, or even your favorite comic-book characters hug it out with just a few clicks. This also means you can tailor these videos to cater to a wide range of use cases such as marketing campaigns, personal messages, family moments, and much more. How Does Pollo AI's AI Hug Video Generator Work? Pollo AI is the leading AI video generator currently on the market. Its AI hugging video generator uses highly sophisticated algorithms that can analyze your static character images and their positions to create realistic AI-hugging animations. This enables you to consistently produce heartwarming AI hug videos that can effectively keep viewers engaged. How To Create AI Hugging Videos Online for Free? Pollo AI is designed to be extremely user-friendly, which means you can generate AI hug videos in just a few clicks. You don’t even need any video editing experience to get started. Step 1: Head to the Pollo AI's AI hugging video generator . Next up, you will need to prepare two high-quality images with two characters. You will also need to set their aspect ratio, in terms of length and width. Pollo AI's AI hugging video generator Pollo AI's AI hugging video generator Step 2: Write a detailed prompt to describe how you want the AI hug video to appear. Try to be as detailed as possible by mentioning background, character behavior, etc. Alternatively, you can use the default prompt provided by the platform. Step 3: Click on ‘Create’ to start the generation process. Normally it takes 10 credits to generate the AI hugging video. Within minutes, the tool will generate an AI hug video. You can also find the final output in the ‘My Creations’ section. Is Pollo AI The Best Way To Create AI Hug Videos? With this AI hug video generator, you can create quality videos that boost audience engagement. This is because the tool does well to analyze your detailed prompts and produce tailored AI hug videos that effectively mirror your desired requirements. This makes it easier to create personalized videos that speak well to audience interests. Aside from that, creating quality AI hug videos can be a time-consuming process but with Pollo AI, it takes only a matter of minutes to get the video output you seek. Moreover, the platform is fast and efficient, so you can consistently generate multiple videos to accommodate any bulk video content needs. On top of that, Pollo AI is a versatile tool that caters to any potential use case. You can use it to create AI hug videos with real people or even animated characters and it will consistently deliver realistic and memorable output. Suffice it to say that Pollo AI can be relied on to create AI hug videos that will keep people entertained. What Other Unique Features Does Pollo AI Offer? There are several unique features that Pollo AI stands out for in video creation. So, let’s quickly break down some of its key capabilities in detail below. Image To Video With Pollo AI, you can instantly transform static images into high-resolution videos. It lets you convert real-life or even animated photos to create moving characters with realistic expressions and motions in just a few clicks. Text To Video You can use simple text prompts to create all sorts of engaging videos. All you need to do is describe what you want to see, eg. characters, backgrounds, actions, etc. The more detailed the prompt, the more accurate the final video output will be. Video To Video You can use Pollo AI to recreate any existing videos into clay, pencil, pop, pixel, anime, or even Disney Pixar-style animations. All it takes is a few clicks to reimagine any real-life footage, making it more dramatic and stylistic enough to captivate audiences. AI Video Templates Pollo AI has numerous AI video templates that can help you create fun and memorable videos. For instance, in addition to AI hugging videos, you can also create AI kissing and handshake videos you can access AI Squish , AI Inflate , AI Explode , and even AI Melt effects to transform images into entertaining and attention-grabbing videos within minutes. AI Squish AI Squish AI Inflate AI Inflate AI Explode AI Explode AI Melt effects AI Melt effects Conclusion AI hug videos can be a great way to garner emotion and relatability with audiences online. Whether it’s creating memorable family videos or entertaining animated videos, with the right tool you can produce emotional and believable AI hugging scenes. As we’ve established, Pollo AI can produce quality results with little to no delay. All you need is to follow the steps above and you can craft stunning AI-hugging videos without any hassle.

How to Create Realistic AI Hug Videos for Free with Pollo AI

Pollo AI এর সাথে বিনামূল্যের জন্য বাস্তবসম্মত AI আলিঙ্গনের ভিডিও কীভাবে তৈরি করবেন৷

Sådan opretter du realistiske AI-knus-videoer gratis med Pollo AI

So erstellen Sie mit Pollo AI kostenlos realistische KI-Umarmungsvideos

Cómo crear vídeos realistas de abrazos con IA de forma gratuita con Pollo AI

Nola sortu AI Hug bideo errealistak doan Pollo AI-rekin

Como crear vídeos de abrazos AI realistas de balde con Pollo AI

Pollo AI を使ってリアルな AI ハグ動画を無料で作成する方法

Pollo AI ашиглан бодитой AI тэврэх видеог хэрхэн яаж хийх вэ

د Pollo AI سره د وړیا لپاره د ریښتیني AI هګ ویډیوګانې رامینځته کولو څرنګوالی

Nigute ushobora gukora amashusho yukuri ya AI Guhobera kubuntu hamwe na Pollo AI

Maitiro ekugadzira Realistic AI Hug Vhidhiyo Yemahara nePollo AI

Jinsi ya Kuunda Video za Kukumbatia za AI Bila Malipo na Pollo AI

​​Easily Scale Real-Time Updates with Firestore: A Maintenance-Free Alternative to WebSockets

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 3. SwitchLight We introduce SwitchLight, a state-of-the-art framework for human portrait relighting, with its architectural overview presented in Fig. 2. We first provide foundational concepts in Sec. 3.1, and define the problem in Sec. 3.2. This is followed by detailing the architecture in Sec. 3.3, and lastly, we describe the loss functions used in Sec. 3.4. 3.1. Preliminaries In this section, vectors n, v, l, and h are denoted as unit vectors. Specifically, n represents the surface normal, v is the view direction, l is the incident light direction, and h is the half-vector computed from l and v. The dot product is clamped between [0..1], indicated by ⟨·⟩. Image Rendering. The primary goal of image rendering is to create a visual representation that accurately simulates the interactions between light and surfaces. These complex interactions are encapsulated by the rendering equation: A surface intrinsically exhibits both diffuse and specular reflections. The diffuse component uniformly scatters light, ensuring consistent illumination regardless of the viewing angle. In contrast, the specular component is viewing angle-dependent, producing shiny highlights that are crucial for achieving a photorealistic effect. Lambertian Diffuse Reflectance. Lambertian reflectance, a standard model for diffuse reflection, describes a uniform light scatter irrespective of the viewing angle. This ensures a consistent appearance in brightness: Here, σ is the albedo, indicating the intrinsic color and brightness of the surface. Cook-Torrance Specular Reflectance. The Cook-Torrance model, based on microfacet theory, represents surfaces as a myriad of tiny, mirror-like facets. It incorporates a roughness parameter α, which allows precise rendering of surface specular reflectance: In this model, D is the microfacet distribution function, describing the orientation of the microfacets relative to the half-vector h, G is the geometric attenuation factor, accounting for the shadowing and masking of microfacets, and F is the Fresnel term, calculating the reflectance variation depending on the viewing angle, where f0 is the surface Fresnel reflectivity at normal incidence. A lower α value implies a smoother surface with sharper specular highlights, whereas a higher α value indicates a rougher surface, resulting in more diffused reflections. By adjusting α, the Cook-Torrance model can depict a range of specular reflections. Image Formation. Upon the base rendering equation, we include the diffuse and specular components of the BRDF and derive a unified formula: where E(l) denotes the incident environmental lighting. This formula represents the core principle that an image is a product of interplay between the BRDF and lighting. To further clarify this concept, we introduce a rendering function R, succinctly modeling the process of image formation: It is important to note that since the BRDF is a function of surface properties, as detailed in Eqn. 3 and 4, we can now clearly understand that image formation is essentially governed by the interaction of surface attributes and lighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 3 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 3. SwitchLight We introduce SwitchLight, a state-of-the-art framework for human portrait relighting, with its architectural overview presented in Fig. 2. We first provide foundational concepts in Sec. 3.1, and define the problem in Sec. 3.2. This is followed by detailing the architecture in Sec. 3.3, and lastly, we describe the loss functions used in Sec. 3.4. 3.1. Preliminaries In this section, vectors n, v, l, and h are denoted as unit vectors. Specifically, n represents the surface normal, v is the view direction, l is the incident light direction, and h is the half-vector computed from l and v. The dot product is clamped between [0..1], indicated by ⟨·⟩. Image Rendering. The primary goal of image rendering is to create a visual representation that accurately simulates the interactions between light and surfaces. These complex interactions are encapsulated by the rendering equation: Image Rendering. A surface intrinsically exhibits both diffuse and specular reflections. The diffuse component uniformly scatters light, ensuring consistent illumination regardless of the viewing angle. In contrast, the specular component is viewing angle-dependent, producing shiny highlights that are crucial for achieving a photorealistic effect. Lambertian Diffuse Reflectance. Lambertian reflectance, a standard model for diffuse reflection, describes a uniform light scatter irrespective of the viewing angle. This ensures a consistent appearance in brightness: Lambertian Diffuse Reflectance. Here, σ is the albedo , indicating the intrinsic color and brightness of the surface. albedo Cook-Torrance Specular Reflectance. The Cook-Torrance model, based on microfacet theory, represents surfaces as a myriad of tiny, mirror-like facets. It incorporates a roughness parameter α, which allows precise rendering of surface specular reflectance: Cook-Torrance Specular Reflectance. In this model, D is the microfacet distribution function, describing the orientation of the microfacets relative to the half-vector h, G is the geometric attenuation factor, accounting for the shadowing and masking of microfacets, and F is the Fresnel term, calculating the reflectance variation depending on the viewing angle, where f0 is the surface Fresnel reflectivity at normal incidence. A lower α value implies a smoother surface with sharper specular highlights, whereas a higher α value indicates a rougher surface, resulting in more diffused reflections. By adjusting α, the Cook-Torrance model can depict a range of specular reflections. Image Formation. Upon the base rendering equation, we include the diffuse and specular components of the BRDF and derive a unified formula: Image Formation. where E(l) denotes the incident environmental lighting. This formula represents the core principle that an image is a product of interplay between the BRDF and lighting. To further clarify this concept, we introduce a rendering function R, succinctly modeling the process of image formation: It is important to note that since the BRDF is a function of surface properties, as detailed in Eqn. 3 and 4, we can now clearly understand that image formation is essentially governed by the interaction of surface attributes and lighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

This New Tech Makes Faces in Photos Look Incredibly Realistic

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References Abstract We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a selfsupervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism. 1. Introduction Relighting is more than an aesthetic tool; it unlocks infinite narrative possibilities and enables seamless integration of subjects into diverse environments (see Fig. 1). This advancement resonates with our innate desire to transcend the physical constraints of space and time, while also providing tangible solutions to practical challenges in digital content creation. It is particularly transformative in virtual (VR) and augmented reality (AR) applications, where relighting facilitates the real-time adaptation of lighting, ensuring that users and digital elements coexist naturally within any environment, offering a next level of telepresence. In this work, we focus on human portrait relighting. While the relighting task fundamentally demands an indepth understanding of geometry, material properties, and illumination, the challenge is more compounded when addressing human subjects, due to the unique characteristics of skin surfaces as well as the diverse textures and reflectance properties of a wide array of clothing, hairstyles, and accessories. These elements interact in complex ways, necessitating advanced algorithms capable of simulating the subtle interplay of light with these varied surfaces. Currently, the most promising approach involves the use of deep neural networks trained on pairs of high-quality relit portrait images and their corresponding intrinsic attributes, which are sourced from a light stage setup [10]. Initial efforts approached the relighting process as a ‘black box’ [45, 48], without delving into the underlying mechanisms. Later advancements adopted a physics-guided model design, incorporating the explicit modeling of image intrinsics and image formation physics [32]. Pandey et al. [34] proposed the Total Relight (TR) architecture, also physics-guided, which decomposes an input image into surface normals and albedo maps, and performs relighting based on the Phong specular reflectance model. The TR architecture has become foundational model for image relighting, with most recent and advanced architectures building upon its principle [23, 31, 52]. Following the physics-guided approach, our contribution lies in a co-design of architecture with a self-supervised pre-training framework. First, our architecture evolves towards a more accurate physical model by integrating the Cook-Torrance specular reflectance model [8], representing a notable advancement from the empirical Phong specular model [37] employed in the Total Relight architecture. The Cook-Torrance model adeptly simulates light interactions with surface microfacets, accounting for spatially varying roughness and reflectivity. Secondly, our pretraining framework scales the learning process beyond the typically hard-to-obtain lightstage data. By revisiting the masked autoencoder (MAE) framework [19], we adept it for the task of relighting. These modifications are crafted to address the unique challenges posed by this task, enabling our model to learn from unlabelled data and refine its ability to produce realistic relit portraits during fine-tuning. To the best of our knowledge, this is the first time applying self-supervised pre-training specifically to the relighting task. To summarize, our contribution is twofold. Firstly, by enhancing the physical reflectance model, we have introduced a new level of realism in the output. Secondly, by adopting self-supervised learning, we have expanded the scale of the training data and enhanced the expression of lighting in diverse real-world scenarios. Collectively, these advancements have led SwitchLight framework to achieve a new state-of-the-art in human portrait relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 1 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References Abstract We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a selfsupervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism. We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a selfsupervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism. 1. Introduction Relighting is more than an aesthetic tool; it unlocks infinite narrative possibilities and enables seamless integration of subjects into diverse environments (see Fig. 1). This advancement resonates with our innate desire to transcend the physical constraints of space and time, while also providing tangible solutions to practical challenges in digital content creation. It is particularly transformative in virtual (VR) and augmented reality (AR) applications, where relighting facilitates the real-time adaptation of lighting, ensuring that users and digital elements coexist naturally within any environment, offering a next level of telepresence. In this work, we focus on human portrait relighting. While the relighting task fundamentally demands an indepth understanding of geometry, material properties, and illumination, the challenge is more compounded when addressing human subjects, due to the unique characteristics of skin surfaces as well as the diverse textures and reflectance properties of a wide array of clothing, hairstyles, and accessories. These elements interact in complex ways, necessitating advanced algorithms capable of simulating the subtle interplay of light with these varied surfaces. Currently, the most promising approach involves the use of deep neural networks trained on pairs of high-quality relit portrait images and their corresponding intrinsic attributes, which are sourced from a light stage setup [10]. Initial efforts approached the relighting process as a ‘black box’ [45, 48], without delving into the underlying mechanisms. Later advancements adopted a physics-guided model design, incorporating the explicit modeling of image intrinsics and image formation physics [32]. Pandey et al. [34] proposed the Total Relight (TR) architecture, also physics-guided, which decomposes an input image into surface normals and albedo maps, and performs relighting based on the Phong specular reflectance model. The TR architecture has become foundational model for image relighting, with most recent and advanced architectures building upon its principle [23, 31, 52]. Following the physics-guided approach, our contribution lies in a co-design of architecture with a self-supervised pre-training framework. First, our architecture evolves towards a more accurate physical model by integrating the Cook-Torrance specular reflectance model [8], representing a notable advancement from the empirical Phong specular model [37] employed in the Total Relight architecture. The Cook-Torrance model adeptly simulates light interactions with surface microfacets, accounting for spatially varying roughness and reflectivity. Secondly, our pretraining framework scales the learning process beyond the typically hard-to-obtain lightstage data. By revisiting the masked autoencoder (MAE) framework [19], we adept it for the task of relighting. These modifications are crafted to address the unique challenges posed by this task, enabling our model to learn from unlabelled data and refine its ability to produce realistic relit portraits during fine-tuning. To the best of our knowledge, this is the first time applying self-supervised pre-training specifically to the relighting task. To summarize, our contribution is twofold. Firstly, by enhancing the physical reflectance model, we have introduced a new level of realism in the output. Secondly, by adopting self-supervised learning, we have expanded the scale of the training data and enhanced the expression of lighting in diverse real-world scenarios. Collectively, these advancements have led SwitchLight framework to achieve a new state-of-the-art in human portrait relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

New Framework by Beeble Researchers Promises to Bring Realistic Glow to Digital Portraits Using AI

Nuwe raamwerk deur Beeble Researchers belowe om realistiese gloed na digitale portrette te bring met behulp van AI

Machaq Marco Beeble Yatxatirinakax AI apnaqasa Retratos Digitales ukar chiqpach qhant’ayañ arsuwayi

Un nuevo marco de trabajo de los investigadores de Beeble promete aportar un brillo realista a los retratos digitales mediante inteligencia artificial

מסגרת חדשה מאת Beeble Researchers מבטיחה להביא זוהר ריאליסטי לפורטרטים דיגיטליים באמצעות AI

Beeble-ի հետազոտողների նոր շրջանակը խոստանում է իրատեսական փայլ հաղորդել թվային դիմանկարներին՝ օգտագործելով AI

Beeble 研究者による新しいフレームワークは、AI を使用してデジタル ポートレートにリアルな輝きをもたらすことを約束します

Cadre ya sika ya ba chercheurs ya Beeble elaki ko mema éclat réaliste na ba portraits numériques en utilisant AI

Ny rafitra vaovao nataon'ny Beeble Researchers dia mampanantena fa hitondra hazavana tena misy amin'ny sary nomerika amin'ny fampiasana AI

Новая платформа от исследователей Beeble обещает придать реалистичное свечение цифровым портретам с помощью ИИ

Ibikorwa bishya byakozwe nabashakashatsi ba Beeble basezeranya kuzana umucyo nyawo ku mashusho ya Digital ukoresheje AI

Chimiro Chitsva neBeeble Vanotsvaga Vanovimbisa Kuunza Kupenya Kwechokwadi kune Digital Portraits Uchishandisa AI.

Rimba Lerintshwa hi Valavisisi va Beeble Ri Tshembisa ku Tisa Ku Vonakala ka Xiviri eka Swifaniso swa Dijitali hi ku Tirhisa AI

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Human Portrait Relighting Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Self-supervised Pre-training Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Beeble Researchers Use Physics-Based Models to Achieve Realistic Lighting Effects in Images

Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. [6] https://github.com/CompVis/stable-diffusion Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 2 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 2 Related Work Early text-to-image generative models, such as DALL-E (Ramesh et al., 2021) and CogView (Ding et al., 2021), or later Parti (Yu et al., 2022) employed autoregressive approaches but often suffered from significant content-level artifacts. This led to the development of a new breed of models that utilized the diffusion process to enhance image quality. Diffusion-based models, such as DALLE 2 (Ramesh et al., 2022), Imagen (Saharia et al., 2022b), and Stable Diffusion[6], have since become cornerstones in this domain. These models are typically divided into pixel-level (Ramesh et al., 2022; Saharia et al., 2022b) and latent-level (Rombach et al., 2022) approaches. This surge of interest has led to the design of innovative approaches and architectures, paving the way for numerous applications based on opensource generative models, such as DreamBooth (Ruiz et al., 2023) and DreamPose (Karras et al., 2023). These applications exploit image generation techniques to offer remarkable features, further fueling the popularity and the rapid development of diffusion-based image generation approaches. This enabled a wide array of applications like 3D object synthesis (Poole et al., 2023; Tang et al., 2023; Lin et al., 2022; Chen et al., 2023), video generation (Ho et al., 2022b; Luo et al., 2023; Ho et al., 2022a; Singer et al., 2023; Blattmann et al., 2023; Esser et al., 2023), controllable image editing (Hertz et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al., 2023; Lu et al., 2023), and more which are now at the forefront of this domain. Diffusion models achieve state-of-the-art results in image generation task both unconditional (Ho et al., 2020; Nichol and Dhariwal, 2021) and conditional (Peebles and Xie, 2022). They beat GANs (Goodfellow et al., 2014) by generating images with better scores of fidelity and diversity without adversarial training (Dhariwal and Nichol, 2021). Diffusion models also show the best performance in various image processing tasks like inpainting, outpainting, and super-resolution (Batzolis et al., 2021; Saharia et al., 2022a). Text-to-image diffusion models have become a popular research direction due to the high performance of diffusion models and the ability to simply integrate text conditions with the classifierfree guidance algorithm (Ho and Salimans, 2022). Early models like GLIDE (Nichol et al., 2022), Imagen (Saharia et al., 2022b), DALL-E 2 (Ramesh et al., 2022) and eDiff-I (Balaji et al., 2022) generate low-resolution image in pixel space and then upsample it with another super-resolution diffusion models. They are also using different text encoders, large language model T5 (Raffel et al., 2020) in Imagen, CLIP (Radford et al., 2021) in GLIDE and DALL-E 2. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [6] https://github.com/CompVis/stable-diffusion

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