Keywords: Computer Vision, Test-Time Training, Masked Auto-Encoder
TL;DR: We show how applying masked autoencoding to train on each unlabeled test sample before making a prediction improves generalization.
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.
Supplementary Material: zip
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