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Poster
Test-Time Training with Masked Autoencoders
Yossi Gandelsman · Yu Sun · Xinlei Chen · Alexei Efros
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.
Author Information
Yossi Gandelsman (UC Berkeley)
Yu Sun (UC Berkeley)
Xinlei Chen (Facebook AI Research)
Alexei Efros (UC Berkeley)
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