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arXiv · 2204.07141

Masked Siamese Networks for Label-Efficient Learning

Mahmoud Assran·Mathilde Caron·Ishan Misra·Piotr Bojanowski·Florian Bordes·Pascal Vincent·Armand Joulin·Michael Rabbat·Nicolas Ballas
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Abstract

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.

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Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas. 2022-04-14. Masked Siamese Networks for Label-Efficient Learning. https://arxiv.org/abs/2204.07141

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