mmselfsup/configs/selfsup/mae

MAE

Masked Autoencoders Are Scalable Vision Learners

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 3× 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 pretraining and shows promising scaling behavior.

Models and Benchmarks

Algorithm	Backbone	Epoch	Batch Size	Results (Top-1 %)		Links
				Linear Eval	Fine-tuning	Pretrain	Linear Eval	Fine-tuning
MAE	ViT-base	300	4096	60.8	83.1	config | model | log	config | model | log	config | model | log
	ViT-base	400	4096	62.5	83.3	config | model | log	config | model | log	config | model | log
	ViT-base	800	4096	65.1	83.3	config | model | log	config | model | log	config | model | log
	ViT-base	1600	4096	67.1	83.5	config | model | log	config | model | log	config | model | log
	ViT-large	400	4096	70.7	85.2	config | model | log	config | model | log	config | model | log
	ViT-large	800	4096	73.7	85.4	config | model | log	config | model | log	config | model | log
	ViT-large	1600	4096	75.5	85.7	config | model | log	config | model | log	config | model | log
	ViT-huge-FT-224	1600	4096	/	86.9	config | model | log	/	config | model | log
	ViT-huge-FT-448	1600	4096	/	87.3	config | model | log	/	config | model | log

Citation

@article{He2021MaskedAA,
  title={Masked Autoencoders Are Scalable Vision Learners},
  author={Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and
  Piotr Doll'ar and Ross B. Girshick},
  journal={ArXiv},
  year={2021}
}