Contrastive Representation Learning: A Framework and Review

Understand

Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain.

• However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned across many fields and domains including Metric Learning and natural language processing.
• In this paper we provide a comprehensive literature review and we propose a general Contrastive Representation Learning framework that simplifies and unifies many different contrastive learning methods.
• We also provide a taxonomy for each of the components of contrastive learning in order to summarise it and distinguish it from other forms of machine learning.