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ClusT3: Information Invariant Test-Time Training

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arxiv 2310.12345 v1 pith:PLNZPP4N submitted 2023-10-18 cs.CV cs.AIcs.LG

Gustavo A. Vargas Hakim , David Osowiechi , Mehrdad Noori , Milad Cheraghalikhani , Ismail Ben Ayed , Christian Desrosiers This is my paper
classification cs.CVcs.AIcs.LG
keywords test-timetasktraininginformationperformanceadaptationattemptauxiliary
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 6 Pith papers

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