Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling 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. Alternatively, many poorly curated training sets containing noisy labels are readily available to be used to build new models. However, the successful exploration of such noisy-label training sets depends on the development of algorithms and models that are robust to these noisy labels.

Machine learning and Noisy Labels: Definitions, Theory, Techniques and Solutions defines 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 developed in the field.

This book is an ideal introduction to machine learning with noisy labels suitable for senior undergraduates, post graduate students, researchers and practitioners using, and researching into, machine learning 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