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Contrastive Representation Learning: A Framework and Review
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DOI：10.1109/ACCESS.2020.3031549.png)
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Abstract
En 中文
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. We then discuss the inductive biases which 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. Finally, we discuss the challenges and some of the most promising future research directions ahead.
Keywords:
Task analysis
Feature extraction
Computational modeling
Data models
Machine learning
Learning systems
Natural language processing
Contrastive learning
representation learning
self-supervised learning
unsupervised learning
deep learning
machine learning
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