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.

深度学习模型在各种视觉任务中展现出卓越的性能，但它们常常容易受到测试时的领域转移所影响。为了缓解这些脆弱性，已经开发了测试时训练（TTT）方法，在训练时同时解决了主任务和次要任务，以在测试时作为自监督代理任务。在本文中，我们提出了一种基于多尺度特征图和离散潜在表示之间相互信息最大化的新型无监督 TTT 技术，它可以作为一项辅助聚类任务整合到标准训练中。实验结果表明，在不同的常见测试时适应基准上具有竞争力的分类性能。