Contrastive Representation Learning: A Framework and Review
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Abstract
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…
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Topics
Keywords
- Computer science
- Artificial intelligence
- Natural language processing
- Active learning (machine learning)
- Contrastive analysis
- Representation (politics)
- Algorithmic learning theory
- Feature learning
UN Sustainable Development Goals
- Quality Education
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