  Despite their recent success, deep neural networks continue to perform poorly
when they encounter distribution shifts at test time. Many recently proposed
approaches try to counter this by aligning the model to the new distribution
prior to inference. With no labels available this requires unsupervised
objectives to adapt the model on the observed test data. In this paper, we
propose Test-Time Self-Training (TeST): a technique that takes as input a model
trained on some source data and a novel data distribution at test time, and
learns invariant and robust representations using a student-teacher framework.
We find that models adapted using TeST significantly improve over baseline
test-time adaptation algorithms. TeST achieves competitive performance to
modern domain adaptation algorithms, while having access to 5-10x less data at
time of adaption. We thoroughly evaluate a variety of baselines on two tasks:
object detection and image segmentation and find that models adapted with TeST.
We find that TeST sets the new state-of-the art for test-time domain adaptation
algorithms.
