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.

翻译：深度学习模型在广泛的视觉任务中展现了卓越的性能，但在测试时往往对领域偏移敏感。为缓解此类脆弱性，研究者提出了测试时训练（TTT）方法，该方法在训练阶段同时解决主任务与辅助任务，以便在测试阶段将辅助任务作为自监督代理任务使用。本研究提出一种新颖的无监督TTT技术，该技术基于多尺度特征图与离散潜在表示之间互信息的最大化，可作为辅助聚类任务集成至标准训练流程。实验结果表明，在多个流行的测试时自适应基准上，该方法实现了具有竞争力的分类性能。