Machine Learning with Noisy Labels: Definitions, Theory, Techniques and Solutions

Machine Learning with Noisy Labels: Definitions, Theory, Techniques and Solutions
by: Gustavo Carneiro (Author)
Publisher: Academic Press
Edition: 1st
Publication Date: 2024/4/1
Language: English
Print Length: 312 pages
ISBN-10: 0443154414
ISBN-13: 9780443154416
Book Description
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 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 setsGives an understanding of the theory of, and motivation for, noisy-label learningShows how to classify noisy-label learning methods into a set of core techniques
About the Author
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 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 setsGives an understanding of the theory of, and motivation for, noisy-label learningShows how to classify noisy-label learning methods into a set of core techniques Read more