On Mutual Information in Contrastive Learning for Visual Representations

27 May 2020 Mike Wu Chengxu Zhuang Milan Mosse Daniel Yamins Noah Goodman

In recent years, several unsupervised, "contrastive" learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks. We show that this family of algorithms maximizes a lower bound on the mutual information between two or more "views" of an image where typical views come from a composition of image augmentations... (read more)

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