Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers

• URL: http://arxiv.org/abs/2301.01431v1
• Date: Wed, 4 Jan 2023 03:59:17 GMT
• Title: Semi-MAE: Masked Autoencoders for Semi-supervised Vision Transformers
• Authors: Haojie Yu, Kang Zhao, Xiaoming Xu
• Abstract summary: Semi-MAE is a pure ViT-based SSL framework consisting of a parallel MAE branch to assist the visual representation learning. Semi-MAE achieves 75.9% top-1 accuracy on ImageNet with 10% labels, surpassing prior state-of-the-art in semi-supervised image classification.
• Score: 5.29690621203603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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