Benutzerdefiniertes Cover
Machine Learning with Noisy Labels : Definitions, Theory, Techniques and Solutions
Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Nicht definiert Verlag: [Erscheinungsort nicht ermittelbar] : Elsevier Science and Technology, 2024Beschreibung: 1 Online-RessourceISBN:- 9780443154423
- 0443154422
- 006.31
- 006.3/1 23
- Q325.5
Inhalte:
PPN: PPN: 1907958193Package identifier: Produktsigel: ZDB-4-NLEBK | BSZ-4-NLEBK-KAUB
Front Cover -- Machine Learning With Noisy Labels -- Copyright -- Contents -- Biography -- Preface -- Acknowledgments -- Mathematical notation -- 1 Problem definition -- 1.1 Motivation -- 1.2 Introduction -- 1.3 Challenges -- 1.4 Conclusion -- 2 Noisy-label problems and datasets -- 2.1 Introduction -- 2.2 Regression, classification, segmentation, and detection problems -- 2.2.1 Regression -- 2.2.2 Classification -- 2.2.3 Semantic segmentation -- 2.2.4 Detection -- 2.3 Label noise problems -- 2.4 Closed set label noise problems -- 2.4.1 Symmetric -- 2.4.2 Asymmetric -- 2.4.3 Instance-dependent
2.5 Open-set label noise problems -- 2.5.1 Open-set symmetric -- 2.5.2 Open-set asymmetric -- 2.5.3 Open-set instance-dependent -- 2.6 Label noise problem setup -- 2.7 Datasets and benchmarks -- 2.7.1 Computer vision datasets and benchmarks -- 2.7.1.1 Classification benchmarks -- 2.7.1.2 Segmentation and detection benchmarks -- 2.7.2 Medical image analysis datasets and benchmarks -- 2.7.2.1 Classification benchmarks -- 2.7.3 Medical image analysis segmentation benchmarks -- 2.7.4 Non-image datasets and benchmarks -- 2.8 Evaluation -- 2.8.1 Classification evaluation
2.8.2 Segmentation evaluation -- 2.8.3 Detection evaluation -- 2.9 Conclusion -- 3 Theoretical aspects of noisy-label learning -- 3.1 Introduction -- 3.2 Bias variance decomposition -- 3.2.1 Regression -- 3.2.2 Classification -- 3.2.3 Explaining label noise with bias variance decomposition -- 3.3 The identifiability of the label transition distribution -- 3.4 PAC learning and noisy-label learning -- 3.5 Conclusion -- 4 Noisy-label learning techniques -- 4.1 Introduction -- 4.2 Loss function -- 4.2.1 Label noise robust loss -- 4.2.2 Loss regularization -- 4.2.3 Loss re-weighting
4.2.4 Loss correction -- 4.2.5 Domain adaptation/generalization -- 4.3 Data processing -- 4.3.1 Adversarial training -- 4.3.2 Data cleaning -- 4.3.3 Sample selection -- 4.3.4 Multi-rater learning -- 4.3.5 Prior knowledge -- 4.3.6 Data augmentation -- 4.3.7 Pseudo-labeling -- 4.4 Training algorithms -- 4.4.1 Meta-learning -- 4.4.2 Self-supervised pre-training -- 4.4.3 Multi-model training -- 4.4.4 Semi-supervised learning -- 4.4.5 Probabilistic graphical model -- 4.4.6 Active learning -- 4.5 Model architecture -- 4.5.1 Label transition methods -- 4.5.2 Graph-based models -- 4.6 Conclusions
5 Benchmarks, methods, results, and code -- 5.1 Introduction -- 5.2 Closed set label noise problems -- 5.3 Open set label noise problems -- 5.4 Imbalanced noisy-label problems -- 5.5 Noisy multi-label learning -- 5.6 Noisy-label segmentation problems -- 5.7 Noisy-label detection problems -- 5.8 Noisy-label medical image segmentation problems -- 5.9 Non-image noisy-label problems -- 5.10 Conclusion -- 6 Conclusions and final considerations -- 6.1 Conclusions -- 6.2 Final considerations and future work -- Bibliography -- Index -- Back Cover
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