Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contrastive Representation Learning: A Framework and Review   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.

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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: ao-sci