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ClusT3: Information Invariant Test-Time Training

Gustavo A. Vargas Hakim·David Osowiechi·Mehrdad Noori·Milad Cheraghalikhani·Ismail Ben Ayed·Christian Desrosiers
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Abstract

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.

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Gustavo A. Vargas Hakim, David Osowiechi, Mehrdad Noori, Milad Cheraghalikhani, Ismail Ben Ayed, Christian Desrosiers. 2023-10-18. ClusT3: Information Invariant Test-Time Training. https://arxiv.org/abs/2310.12345

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