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 …

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 …