Deep learning:
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; San Diego ; Kidlington ; London
Academic Press
[2023]
|
| Schriftenreihe: | Handbook of statistics
volume 48 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xv, 252 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780443184307 |
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Datensatz im Suchindex
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Contents Contributors Preface x¡ xüi Section I Foundations 1. 2. Exact deep learning machines 1 Ami S.R. Srinivasa Rao 1. Introduction 2. EDLM constructions 3. Conclusions References 1 2 6 7 Multiscale representation learning for biomedical analysis Abhishek Singh, Utkarsh Porwał, Anurag Bhardwaj, and Wei Jin 1. Introduction 2. Representation learning: Background 3. Multiscale embedding motivation 4. Theoretical framework 4.1 Local context embedding 4.2 Wide context embedding 4.3 Multiscale embedding 4.4 Postprocessing and inference for word similarity task 4.5 Evaluation scheme 5. Experiments, results, and discussion 5.1 Datasets 5.2 Wide context embedding (context2vec) 5.3 Quantitative evaluation 5.4 Qualitative analysis 5.5 Error analysis 6. Conclusion and future work References 9 9 12 13 15 15 15 17 17 17 18 18 19 20 23 24 24 25 v
Contents ví 3. Adversarial attacks and robust defenses in deep learning 29 Chun Pong Lau, Jiang Liu, Wei-An Lin, Hossein Souri, Pirazh Khorramshahi, and Rama Chellappa 1. Introduction 2. Adversarial attacks 2.1 Fast gradient sign method 2.2 Projected gradient descent 2.3 DeepFool 2.4 Carlini and wagner attack 2.5 Adversarial patch 2.6 Elastic 2.7 Fog 2.8 Snow 2.9 Gabor 2.10 JPEG 3. On-manifold robustness 3.1 Defense-GAN 3.2 Dual manifold adversarial training (DMAT) 4. Knowledge distillation-based defenses 5. Defenses for object detector 6. Reverse engineering of deceptions via residual learning 6.1 Adversarial perturbation estimation 6.2 Experimental evaluation Acknowledgments References 29 32 32 32 32 32 32 33 33 33 33 33 33 33 35 41 44 46 47 52 53 53 Section II Advanced Methods 4. Deep metric learning for computer vision: A brief overview Deen Dayal Mohan, Bhavin Jawade, SrirangaraJ Setlur, and Venu Covindaraju 1. Introduction 2. Background 3. Pair-based formulation 3.1 Contrastive loss 3.2 Triplet loss 3.3 N-pair loss 3.4 Multi-Similarity loss 4. Proxy-based methods 4.1 Proxy-NCA and Proxy-NCA++ 4.2 Proxy Anchor loss 4.3 ProxyGML Loss 5. Regularizations 5.1 Language guidance 5.2 Direction regularization 6. Conclusion References 59 59 61 62 62 62 65 66 68 69 71 72 75 75 76 78 78
Contents 5. Source distribution weighted multisource domain adaptation without access to source data vii 81 Sk Miraj Ahmed, Dripta S. Raychaudhuri, Samet Oymak, and Amit K. Roy-Chowdhury 1. Introduction 1.1 Main contributions 2. Related works 2.1 Unsupervised domain adaptation 2.2 Hypothesis transfer learning 2.3 Multisource domain adaptation 2.4 Source-free multisource UDA 3. Problem setting 4. Practical motivation 5. Overall framework ofDECISION—A review 5.1 Weighted information maximization 5.2 Weighted pseudo-labeling 5.3 Optimization 6. Theoretical insights 6.1 Theoretical motivation behind DECISION 7. Source distribution dependent weights (DECISION-mlp) 8. Proof of Lemma 1 9. Experiments 9.1 Experiments on DECISION 9.2 Implementation details 9.3 Object recognition 9.4 Ablation study 9.5 Results and analyses of DECISION-mlp 10. Conclusions and future work References 82 83 84 84 84 84 85 85 86 86 87 88 89 90 90 93 95 96 96 97 98 98 101 102 103 Section III Transformative Applications 6. Deep learning methods for scientific and industrial research 107 C.K. Patra, Kantha Rao Bhimala, Ashapurna Marndı, Saikat Chowdhury, Jarjish Rahaman, Sutanu Nandi, Ram Rup Sarkar, K.C. Couda, K.V. Ramesh, Rajesh P. Barnwal, Siddhartha Raj, and Anil Saini 1. Introduction 2. Data and methods 2.1 Different types of data for deep learning 2.2 Methodology 3. Applications of DL techniques for multi-disciplinary studies 3.1 Applications of DL models in tumor diagnosis 3.2 Application of DL model for classifying molecular subtypes of glioma tissues 140 108 113 113 119 135 135
Contents viii Application of the deep learning model for the prognosis of glioma patients I40 3.4 Applications of DL model for predicting driver gene mutations in glioma 141 3.5 Application of Time Division LSTM for short-term prediction of wind speed 141 3.6 Application of LSTM for the estimation of crop production 3.7 Classification of tea leaves 3.8 Weather integrated deep learning techniques to predict the COVID-19cases overstates in India 155 4. Discussion and futureprospects Acknowledgments References 3.3 7. On bias and fairness in deep learning-based facial analysis 148 152 159 163 163 169 Surbhi Mittal, Puspita Majumdar, Mayank Vatsa, and Richa Singh 1. Introduction 169 2. Tasks in facial analysis 173 2.1 Face detection and recognition 173 2.2 Attribute prediction 175 3. Facial analysis databases for bias study 175 4. Evaluation metrics 180 4.1 Classification parity-based metrics 180 4.2 Score-based metrics 181 4.3 Facial analysis-specific metrics 182 5. Fairness estimation and analysis 183 5.1 Fairness in face detection and recognition 184 5.2 Fairness in attribute prediction 187 6. Fair algorithms and bias mitigation 191 6.1 Face detection and recognition 191 6.2 Attribute prediction 195 7. Meta-analysis of algorithms 199 8. Topography of commercial systems and patents 202 9. Open challenges 206 9.1 Fairness in presence of occlusion 206 9.2 Fairness across intersectional subgroups 207 9.3 Trade-off between fairness and model performance 207 9.4 Lack of benchmark databases 207 9.5 Variation in evaluation protocols 208 9.6 Unavailability of complete information 208
9.7 Identification of bias in models 208 9.8 Quantification of fairness in datasets 208 10. Discussion 209 Acknowledgment 21 ļ References 2ļ ļ
Contents 8. ix Manipulating faces for identity theft via morphing and deepfake: Digital privacy 223 Akshay Agarwal and Nalini Ratha 1. Introduction 2. Identity manipulation techniques 3. Identity manipulation datasets 4. Identity attack detection algorithms 5. Open challenges 6. Conclusion References Index 223 225 230 233 234 238 238 243 |
| adam_txt |
Contents Contributors Preface x¡ xüi Section I Foundations 1. 2. Exact deep learning machines 1 Ami S.R. Srinivasa Rao 1. Introduction 2. EDLM constructions 3. Conclusions References 1 2 6 7 Multiscale representation learning for biomedical analysis Abhishek Singh, Utkarsh Porwał, Anurag Bhardwaj, and Wei Jin 1. Introduction 2. Representation learning: Background 3. Multiscale embedding motivation 4. Theoretical framework 4.1 Local context embedding 4.2 Wide context embedding 4.3 Multiscale embedding 4.4 Postprocessing and inference for word similarity task 4.5 Evaluation scheme 5. Experiments, results, and discussion 5.1 Datasets 5.2 Wide context embedding (context2vec) 5.3 Quantitative evaluation 5.4 Qualitative analysis 5.5 Error analysis 6. Conclusion and future work References 9 9 12 13 15 15 15 17 17 17 18 18 19 20 23 24 24 25 v
Contents ví 3. Adversarial attacks and robust defenses in deep learning 29 Chun Pong Lau, Jiang Liu, Wei-An Lin, Hossein Souri, Pirazh Khorramshahi, and Rama Chellappa 1. Introduction 2. Adversarial attacks 2.1 Fast gradient sign method 2.2 Projected gradient descent 2.3 DeepFool 2.4 Carlini and wagner attack 2.5 Adversarial patch 2.6 Elastic 2.7 Fog 2.8 Snow 2.9 Gabor 2.10 JPEG 3. On-manifold robustness 3.1 Defense-GAN 3.2 Dual manifold adversarial training (DMAT) 4. Knowledge distillation-based defenses 5. Defenses for object detector 6. Reverse engineering of deceptions via residual learning 6.1 Adversarial perturbation estimation 6.2 Experimental evaluation Acknowledgments References 29 32 32 32 32 32 32 33 33 33 33 33 33 33 35 41 44 46 47 52 53 53 Section II Advanced Methods 4. Deep metric learning for computer vision: A brief overview Deen Dayal Mohan, Bhavin Jawade, SrirangaraJ Setlur, and Venu Covindaraju 1. Introduction 2. Background 3. Pair-based formulation 3.1 Contrastive loss 3.2 Triplet loss 3.3 N-pair loss 3.4 Multi-Similarity loss 4. Proxy-based methods 4.1 Proxy-NCA and Proxy-NCA++ 4.2 Proxy Anchor loss 4.3 ProxyGML Loss 5. Regularizations 5.1 Language guidance 5.2 Direction regularization 6. Conclusion References 59 59 61 62 62 62 65 66 68 69 71 72 75 75 76 78 78
Contents 5. Source distribution weighted multisource domain adaptation without access to source data vii 81 Sk Miraj Ahmed, Dripta S. Raychaudhuri, Samet Oymak, and Amit K. Roy-Chowdhury 1. Introduction 1.1 Main contributions 2. Related works 2.1 Unsupervised domain adaptation 2.2 Hypothesis transfer learning 2.3 Multisource domain adaptation 2.4 Source-free multisource UDA 3. Problem setting 4. Practical motivation 5. Overall framework ofDECISION—A review 5.1 Weighted information maximization 5.2 Weighted pseudo-labeling 5.3 Optimization 6. Theoretical insights 6.1 Theoretical motivation behind DECISION 7. Source distribution dependent weights (DECISION-mlp) 8. Proof of Lemma 1 9. Experiments 9.1 Experiments on DECISION 9.2 Implementation details 9.3 Object recognition 9.4 Ablation study 9.5 Results and analyses of DECISION-mlp 10. Conclusions and future work References 82 83 84 84 84 84 85 85 86 86 87 88 89 90 90 93 95 96 96 97 98 98 101 102 103 Section III Transformative Applications 6. Deep learning methods for scientific and industrial research 107 C.K. Patra, Kantha Rao Bhimala, Ashapurna Marndı, Saikat Chowdhury, Jarjish Rahaman, Sutanu Nandi, Ram Rup Sarkar, K.C. Couda, K.V. Ramesh, Rajesh P. Barnwal, Siddhartha Raj, and Anil Saini 1. Introduction 2. Data and methods 2.1 Different types of data for deep learning 2.2 Methodology 3. Applications of DL techniques for multi-disciplinary studies 3.1 Applications of DL models in tumor diagnosis 3.2 Application of DL model for classifying molecular subtypes of glioma tissues 140 108 113 113 119 135 135
Contents viii Application of the deep learning model for the prognosis of glioma patients I40 3.4 Applications of DL model for predicting driver gene mutations in glioma 141 3.5 Application of Time Division LSTM for short-term prediction of wind speed 141 3.6 Application of LSTM for the estimation of crop production 3.7 Classification of tea leaves 3.8 Weather integrated deep learning techniques to predict the COVID-19cases overstates in India 155 4. Discussion and futureprospects Acknowledgments References 3.3 7. On bias and fairness in deep learning-based facial analysis 148 152 159 163 163 169 Surbhi Mittal, Puspita Majumdar, Mayank Vatsa, and Richa Singh 1. Introduction 169 2. Tasks in facial analysis 173 2.1 Face detection and recognition 173 2.2 Attribute prediction 175 3. Facial analysis databases for bias study 175 4. Evaluation metrics 180 4.1 Classification parity-based metrics 180 4.2 Score-based metrics 181 4.3 Facial analysis-specific metrics 182 5. Fairness estimation and analysis 183 5.1 Fairness in face detection and recognition 184 5.2 Fairness in attribute prediction 187 6. Fair algorithms and bias mitigation 191 6.1 Face detection and recognition 191 6.2 Attribute prediction 195 7. Meta-analysis of algorithms 199 8. Topography of commercial systems and patents 202 9. Open challenges 206 9.1 Fairness in presence of occlusion 206 9.2 Fairness across intersectional subgroups 207 9.3 Trade-off between fairness and model performance 207 9.4 Lack of benchmark databases 207 9.5 Variation in evaluation protocols 208 9.6 Unavailability of complete information 208
9.7 Identification of bias in models 208 9.8 Quantification of fairness in datasets 208 10. Discussion 209 Acknowledgment 21 ļ References 2ļ ļ
Contents 8. ix Manipulating faces for identity theft via morphing and deepfake: Digital privacy 223 Akshay Agarwal and Nalini Ratha 1. Introduction 2. Identity manipulation techniques 3. Identity manipulation datasets 4. Identity attack detection algorithms 5. Open challenges 6. Conclusion References Index 223 225 230 233 234 238 238 243 |
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| spelling | Deep learning edited by Venu Govindaraju, Arni S. R. Srinivasa Rao, C.R. Rao Cambridge ; San Diego ; Kidlington ; London Academic Press [2023] © 2023 xv, 252 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics volume 48 Deep Learning (DE-588)1135597375 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Deep Learning (DE-588)1135597375 s DE-604 Govindaraju, Venu 1964- (DE-588)1036336638 edt Rao, Arni S. R. Srinivasa (DE-588)1143256220 edt Rao, Calyampudi Radhakrishna 1920-2023 (DE-588)119285924 edt Handbook of statistics volume 48 (DE-604)BV000002510 48 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033784195&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Deep learning Handbook of statistics Deep Learning (DE-588)1135597375 gnd |
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| title | Deep learning |
| title_auth | Deep learning |
| title_exact_search | Deep learning |
| title_exact_search_txtP | Deep learning |
| title_full | Deep learning edited by Venu Govindaraju, Arni S. R. Srinivasa Rao, C.R. Rao |
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