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

by: Gustavo Caeiro (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 Leaing and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine leaing with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, machine leaing methods. Most of the mode machine leaing models based on deep leaing 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 leaingShows how to classify noisy-label leaing methods into a set of core techniques

About the Author

Machine Leaing and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine leaing with noisy labels that is suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching, machine leaing methods. Most of the mode machine leaing models based on deep leaing 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 leaingShows how to classify noisy-label leaing methods into a set of core techniques