  Vision Transformer (ViT) suffers from data scarcity in semi-supervised
learning (SSL). To alleviate this issue, inspired by masked autoencoder (MAE),
which is a data-efficient self-supervised learner, we propose Semi-MAE, a pure
ViT-based SSL framework consisting of a parallel MAE branch to assist the
visual representation learning and make the pseudo labels more accurate. The
MAE branch is designed as an asymmetric architecture consisting of a
lightweight decoder and a shared-weights encoder. We feed the weakly-augmented
unlabeled data with a high masking ratio to the MAE branch and reconstruct the
missing pixels. Semi-MAE achieves 75.9% top-1 accuracy on ImageNet with 10%
labels, surpassing prior state-of-the-art in semi-supervised image
classification. In addition, extensive experiments demonstrate that Semi-MAE
can be readily used for other ViT models and masked image modeling methods.
