Masked Autoencoders are Robust Data Augmentors

• URL: http://arxiv.org/abs/2206.04846v2
• Date: Wed, 16 Apr 2025 07:10:09 GMT
• Title: Masked Autoencoders are Robust Data Augmentors
• Authors: Haohang Xu, Shuangrui Ding, Manqi Zhao, Dongsheng Jiang,
• Abstract summary: We propose a novel perspective of augmentation to regularize the training process.<n>Inspired by the recent success of applying masked image modeling to self-supervised learning, we adopt the self-supervised masked autoencoder.<n>We show that utilizing such model-based nonlinear transformation as data augmentation can improve high-level recognition tasks.
• Score: 9.819398274610933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks are capable of learning powerful representations to tackle complex vision tasks but expose undesirable properties like the over-fitting issue. To this end, regularization techniques like image augmentation are necessary for deep neural networks to generalize well. Nevertheless, most prevalent image augmentation recipes confine themselves to off-the-shelf linear transformations like scale, flip, and colorjitter. Due to their hand-crafted property, these augmentations are insufficient to generate truly hard augmented examples. In this paper, we propose a novel perspective of augmentation to regularize the training process. Inspired by the recent success of applying masked image modeling to self-supervised learning, we adopt the self-supervised masked autoencoder to generate the distorted view of the input images. We show that utilizing such model-based nonlinear transformation as data augmentation can improve high-level recognition tasks. We term the proposed method as \textbf{M}ask-\textbf{R}econstruct \textbf{A}ugmentation (MRA). The extensive experiments on various image classification benchmarks verify the effectiveness of the proposed augmentation. Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification.

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