Masked Autoencoders Are Scalable Vision Learners

• URL: http://arxiv.org/abs/2111.06377v1
• Date: Thu, 11 Nov 2021 18:46:40 GMT
• Title: Masked Autoencoders Are Scalable Vision Learners
• Authors: Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll\'ar, Ross Girshick
• Abstract summary: Masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. Coupling these two designs enables us to train large models efficiently and effectively.
• Score: 60.97703494764904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pre-training and shows promising scaling behavior.

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