Contrastive learning, multi-view redundancy, and linear models

Christopher Tosh
Christopher Tosh

ALT, pp. 1179-1206, 2021.

Cited by: 2|Views14
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Abstract:

Self-supervised learning is an empirically successful approach to unsupervised learning based on creating artificial supervised learning problems. A popular self-supervised approach to representation learning is contrastive learning, which leverages naturally occurring pairs of similar and dissimilar data points, or multiple views of th...More

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