A General Framework for Contrastive Representation Learning
Contrastive Representation Learning: A Framework and Review
In this paper we provide a comprehensive literature review and we propose a general contrastiverepresentation 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.
We then discuss the inductive biaseswhich are present in any contrastive learning system and we analyse our framework under different views from various sub-fields of machine learning.
Examples of how contrastive learning has been applied in computer vision, natural language processing, audio processing, and others, as well as in reinforcement learning are also presented.