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How to Understand Masked Autoencoders

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arxiv 2202.03670 v2 pith:G5HQKL7I submitted 2022-02-08 cs.CV cs.LG

Shuhao Cao , Peng Xu , David A. Clifton This is my paper · ORCID
classification cs.CVcs.LG
keywords maskedautoencodersexplainframeworkmathematicaltheoreticalachievesanswer
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"Masked Autoencoders (MAE) Are Scalable Vision Learners" revolutionizes the self-supervised learning method in that it not only achieves the state-of-the-art for image pre-training, but is also a milestone that bridges the gap between visual and linguistic masked autoencoding (BERT-style) pre-trainings. However, to our knowledge, to date there are no theoretical perspectives to explain the powerful expressivity of MAE. In this paper, we, for the first time, propose a unified theoretical framework that provides a mathematical understanding for MAE. Specifically, we explain the patch-based attention approaches of MAE using an integral kernel under a non-overlapping domain decomposition setting. To help the research community to further comprehend the main reasons of the great success of MAE, based on our framework, we pose five questions and answer them with mathematical rigor using insights from operator theory.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AMRM-Pure: Semantic-Preserving Adversarial Purification

    cs.CR 2026-07 conditional novelty 6.5 of 10

    AMRM-Pure purifies adversarial images by minimizing reconstruction loss of attentive mask models (MAE/MaskDiT) to restore patch-level semantic relations, with optional classifier fine-tuning for SOTA robust accuracy.

  2. Self-Guided Masked Autoencoder

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A Masked Autoencoder that masks the object cluster found by its own early patch-clustering signal learns better representations than random masking, with no external labels or models.

  3. Robust Representation Learning in Masked Autoencoders

    cs.LG 2026-02 conditional novelty 5.0 of 10

    Masked Autoencoders build class-separable representations across depth and keep their embeddings directionally stable under blur and occlusion, which tracks their robust classification.

  4. MINR: Implicit Neural Representations with Masked Image Modelling

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A hybrid of implicit neural representations and masked image modeling, called MINR, reconstructs masked image patches better than MAE in the reported in-domain and out-of-distribution tests with fewer parameters.

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