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. In this work, we
address this issue by introducing a new self-supervised learning strategy,
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 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.

该研究提出了一种新的自监督学习策略，称为 LoGo，它通过不同的本地和全局裁剪来促进特征的多样性，并取得了在 1/10 数据的情况下超越有监督模型的转移学习的最好结果。