ClusT3: Information Invariant Test-Time Training

Abstract

Deep Learning models have shown remarkable performance in a broad range of vision tasks. However, they are often vulnerable to 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. The code can be found at: https://github.com/dosowiechi/ClusT3.git