Web: http://arxiv.org/abs/2203.08777

May 6, 2022, 1:12 a.m. | Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, Relja Arandjelović

cs.LG updates on arXiv.org arxiv.org

The promise of self-supervised learning (SSL) is to leverage large amounts of
unlabeled data to solve complex tasks. While there has been excellent progress
with simple, image-level learning, recent methods have shown the advantage of
including knowledge of image structure. However, by introducing hand-crafted
image segmentations to define regions of interest, or specialized augmentation
strategies, these methods sacrifice the simplicity and generality that makes
SSL so powerful. Instead, we propose a self-supervised learning paradigm that
discovers this image structure by …

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