How to Understand Masked Autoencoders
"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.
Code (0)
등록된 구현이 없습니다.
Tasks
Self-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4…
Object State Change ClassificationTemporal LocalizationVideo UnderstandingDiffusion Models as Masked Autoencoders
There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representations in light of recent interest in denoi…
DenoisingImage InpaintingQuantum Masked Autoencoders for Vision Learning
Classical autoencoders are widely used to learn features of input data. To improve the feature learning, classical masked autoencoders extend classical autoencoders to learn the features of the original input sample in t…
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding. However, there is a lack of theoretical understanding of how masking matters on graph auto…
Contrastive LearningLink PredictionNode ClassificationSelf-Supervised LearningLatent Diffusion Models with Masked AutoEncoders
In spite of the remarkable potential of Latent Diffusion Models (LDMs) in image generation, the desired properties and optimal design of the autoencoders have been underexplored. In this work, we analyze the role of auto…
Image Generation