Abstract
Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores the fact that two crops may truly contain different image information, e.g., background and small objects, and thus tends to restrain the diversity of the learned representations. %To this end, the existing strategies typically employ loss functions that enforces the networks to discard part of valuable information, e.g. background and small objects, and sacrifices the diversity of representation. In this work, we address this issue by introducing a new self-supervised learning strategy, LoGo, that explicitly reasons about {\bf Lo}cal and {\bf G}l{\bf o}bal crops. To achieve view invariance, LoGo encourages similarity between global crops from the same image, as well as between a global and a local crop. However, to correctly encode the fact that the content of smaller crops may differ entirely, LoGo promotes two local crops to have dissimilar representations, while being close to global crops. Our LoGo strategy can easily be applied to existing SSL methods. Our extensive experiments on a variety of datasets and using different self-supervised learning frameworks validate its superiority over existing approaches. Noticeably, we achieve better results than supervised models on transfer learning when using only $1/10$ of the data.
Abstract (translated)
URL
https://arxiv.org/abs/2203.17205
