2022
NIPS
NeurIPS 2022
Test-Time Training with Masked Autoencoders
Abstract
Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision.In this paper, we use masked autoencoders for this one-sample learning problem.Empirically, our simple method improves generalization on many visual benchmarks for distribution shifts.Theoretically, we characterize this improvement in terms of the bias-variance trade-off.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Hot Topic Early Bird
— masked autoencoder
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Application Areas > Domain Adaptation
Deep Learning > Architectures > Autoencoders
Deep Learning > Learning Types > Self-Supervised Learning
Deep Learning > Learning Types > Domain Adaptation
Deep Learning > Models > Autoencoders