Self-Supervised Ranking for Representation Learning

14 Oct 2020 Ali Varamesh Ali Diba Tinne Tuytelaars Luc van Gool

We present a new framework for self-supervised representation learning by formulating it as a ranking problem in an image retrieval context on a large number of random views (augmentations) obtained from images. Our work is based on two intuitions: first, a good representation of images must yield a high-quality image ranking in a retrieval task; second, we would expect random views of an image to be ranked closer to a reference view of that image than random views of other images... (read more)

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