  Deep Learning models have shown remarkable performance in a broad range of
vision tasks. However, they are often vulnerable against domain shifts at
test-time. Test-time training (TTT) methods have been developed in an attempt
to mitigate these vulnerabilities, where a secondary task is solved at training
time simultaneously with the main task, to be later used as an self-supervised
proxy task at test-time. In this work, we propose a novel unsupervised TTT
technique based on the maximization of Mutual Information between multi-scale
feature maps and a discrete latent representation, which can be integrated to
the standard training as an auxiliary clustering task. Experimental results
demonstrate competitive classification performance on different popular
test-time adaptation benchmarks.
