# MAE
> [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377)
## 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
## Citation
```bibtex
@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}
}
```
