Machine Learning with Noisy Labels
Definitions, Theory, Techniques and Solutions
- 1st Edition - February 23, 2024
- Imprint: Academic Press
- Author: Gustavo Carneiro
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 4 4 1 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 5 4 4 2 - 3
Machine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine learning with noisy labels that is suitable for senior… Read more
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Request a sales quoteMachine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine learning with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, machine learning methods. Most of the modern machine learning models based on deep learning techniques depend on carefully curated and cleanly labeled training sets to be reliably trained and deployed. However, the expensive labeling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. This book defines the different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the effective use of noisy-label training sets, and explains the most accurate methods.
- Shows how to design and reproduce regression, classification and segmentation models using large-scale noisy-label training sets
- Gives an understanding of the theory of, and motivation for, noisy-label learning
- Shows how to classify noisy-label learning methods into a set of core techniques
Senior undergraduates, graduate students and researchers in computer vision and medical imaging, machine learning, biomedical engineering, Radiologists
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Biography
- Gustavo Carneiro
- Preface
- Survey papers and books on the same topic
- Book organization
- Bibliography
- Acknowledgments
- Mathematical notation
- Chapter 1: Problem definition
- Abstract
- 1.1. Motivation
- 1.2. Introduction
- 1.3. Challenges
- 1.4. Conclusion
- Bibliography
- Chapter 2: Noisy-label problems and datasets
- Abstract
- 2.1. Introduction
- 2.2. Regression, classification, segmentation, and detection problems
- 2.3. Label noise problems
- 2.4. Closed set label noise problems
- 2.5. Open-set label noise problems
- 2.6. Label noise problem setup
- 2.7. Datasets and benchmarks
- 2.8. Evaluation
- 2.9. Conclusion
- Bibliography
- Chapter 3: Theoretical aspects of noisy-label learning
- Abstract
- 3.1. Introduction
- 3.2. Bias variance decomposition
- 3.3. The identifiability of the label transition distribution
- 3.4. PAC learning and noisy-label learning
- 3.5. Conclusion
- Bibliography
- Chapter 4: Noisy-label learning techniques
- Abstract
- 4.1. Introduction
- 4.2. Loss function
- 4.3. Data processing
- 4.4. Training algorithms
- 4.5. Model architecture
- 4.6. Conclusions
- Bibliography
- Chapter 5: Benchmarks, methods, results, and code
- Abstract
- 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
- Bibliography
- Chapter 6: Conclusions and final considerations
- Abstract
- 6.1. Conclusions
- 6.2. Final considerations and future work
- Bibliography
- Bibliography
- Bibliography
- Index
- Edition: 1
- Published: February 23, 2024
- Imprint: Academic Press
- No. of pages: 312
- Language: English
- Paperback ISBN: 9780443154416
- eBook ISBN: 9780443154423
GC
Gustavo Carneiro
Professor Gustavo Carneiro, Artificial Intelligence and Machine Learning, University of Surrey, UK.
Affiliations and expertise
Professor of AI and Machine Learning, Centre for Vision, Speech and Signal Processing (CVSSP), Surrey Institute for People-centred Artificial Intelligence, Department of Electrical and Electronic Engineering, The University of Surrey, UKRead Machine Learning with Noisy Labels on ScienceDirect