ClusT3: Information Invariant Test-Time Training

• URL: http://arxiv.org/abs/2310.12345v1
• Date: Wed, 18 Oct 2023 21:43:37 GMT
• Title: ClusT3: Information Invariant Test-Time Training
• Authors: Gustavo A. Vargas Hakim and David Osowiechi and Mehrdad Noori and Milad Cheraghalikhani and Ismail Ben Ayed and Christian Desrosiers
• Abstract summary: Test-time training (TTT) methods have been developed in an attempt to mitigate these vulnerabilities. We propose a novel unsupervised TTT technique based on the Mutual of Mutual Information between multi-scale feature maps and a discrete latent representation. Experimental results demonstrate competitive classification performance on different popular test-time adaptation benchmarks.
• Score: 19.461441044484427
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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