  Test-time training provides a new approach solving the problem of domain
shift. In its framework, a test-time training phase is inserted between
training phase and test phase. During test-time training phase, usually parts
of the model are updated with test sample(s). Then the updated model will be
used in the test phase. However, utilizing test samples for test-time training
has some limitations. Firstly, it will lead to overfitting to the test-time
procedure thus hurt the performance on the main task. Besides, updating part of
the model without changing other parts will induce a mismatch problem. Thus it
is hard to perform better on the main task. To relieve above problems, we
propose to use mixup in test-time training (MixTTT) which controls the change
of model's parameters as well as completing the test-time procedure. We
theoretically show its contribution in alleviating the mismatch problem of
updated part and static part for the main task as a specific regularization
effect for test-time training. MixTTT can be used as an add-on module in
general test-time training based methods to further improve their performance.
Experimental results show the effectiveness of our method.
