How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders

• URL: http://arxiv.org/abs/2210.08344v2
• Date: Sun, 26 Mar 2023 06:47:53 GMT
• Title: How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
• Authors: Qi Zhang, Yifei Wang, Yisen Wang
• Abstract summary: Masked Autoencoders (MAE) based on a reconstruction task have risen to be a promising paradigm for self-supervised learning (SSL) We propose a theoretical understanding of how masking matters for MAE to learn meaningful features.
• Score: 21.849681446573257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked Autoencoders (MAE) based on a reconstruction task have risen to be a promising paradigm for self-supervised learning (SSL) and achieve state-of-the-art performance across different benchmark datasets. However, despite its impressive empirical success, there is still limited theoretical understanding of it. In this paper, we propose a theoretical understanding of how masking matters for MAE to learn meaningful features. We establish a close connection between MAE and contrastive learning, which shows that MAE implicit aligns the mask-induced positive pairs. Built upon this connection, we develop the first downstream guarantees for MAE methods, and analyze the effect of mask ratio. Besides, as a result of the implicit alignment, we also point out the dimensional collapse issue of MAE, and propose a Uniformity-enhanced MAE (U-MAE) loss that can effectively address this issue and bring significant improvements on real-world datasets, including CIFAR-10, ImageNet-100, and ImageNet-1K. Code is available at (https://github.com/zhangq327/U-MAE).

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