  We propose Masked Siamese Networks (MSN), a self-supervised learning
framework for learning image representations. Our approach matches the
representation of an image view containing randomly masked patches to the
representation of the original unmasked image. This self-supervised
pre-training strategy is particularly scalable when applied to Vision
Transformers since only the unmasked patches are processed by the network. As a
result, MSNs improve the scalability of joint-embedding architectures, while
producing representations of a high semantic level that perform competitively
on low-shot image classification. For instance, on ImageNet-1K, with only 5,000
annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1%
of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new
state-of-the-art for self-supervised learning on this benchmark. Our code is
publicly available.
