Related papers: Test-Time Training with Masked Autoencoders

Test-Time Training with Masked Autoencoders

URL: http://arxiv.org/abs/2209.07522v1
Date: Thu, 15 Sep 2022 17:59:34 GMT
Title: Test-Time Training with Masked Autoencoders
Authors: Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei A. Efros
Abstract summary: 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.
Score: 54.983147122777574
License: http://creativecommons.org/licenses/by/4.0/
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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