Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similar-ity 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. In this work, we address this issue by introducing a new self-supervised learning strat-egy, LoGo, that explicitly reasons about Local and Global 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 dissimi-lar representations, while being close to global crops. Our LoGo strategy can easily be applied to existing SSL meth-ods. Our extensive experiments on a variety of datasets and using different self-supervised learning frameworks vali-date its superiority over existing approaches. Noticeably, we achieve better results than supervised models on trans-fer learning when using only 1/10 of the data. 11Our code and pretrained models can be found at https://github.com/ztt1024/LoGo-SSL.