  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.
