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How to Understand Masked Autoencoders
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How to Understand Masked Autoencoders
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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.
Forward citations
Cited by 5 Pith papers
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Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data
A self-explainable operator learning method reformulates operators as decomposable integral equations to reveal spatial input contributions to predictions in blood flow and aerodynamics problems.
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AMRM-Pure: Semantic-Preserving Adversarial Purification
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.
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SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning
SingGuard presents a policy-adaptive multimodal LLM guardrail family with hybrid reasoning regimes and a new benchmark of 56,340 examples, claiming SOTA F1 across 35 datasets and improved policy adherence under runtim...
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SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning
SingGuard introduces a policy-adaptive multimodal LLM guardrail with dynamic reasoning regimes and SingGuard-Bench, reporting SOTA F1 scores across 35 datasets and improved policy-following accuracy under runtime shifts.
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Robust Representation Learning in Masked Autoencoders
Masked Autoencoders build class-separable representations across depth and keep their embeddings directionally stable under blur and occlusion, which tracks their robust classification.
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