Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

This work turns a single unlabeled test sample into a self-supervised learning problem, on which the model parameters are updated before making a prediction, which leads to improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts.