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。

ClusT3: 信息不变的测试时间训练

ClusT3: Information Invariant Test-Time Training

The main challenge in domain generalization (DG) is to handle the
distribution shift problem that lies between the training and test data. Recent
studies suggest that test-time training (TTT), which adapts the learned model
with test data, might be a promising solution to the problem. Generally, a TTT
strategy hinges its performance on two main factors: selecting an appropriate
auxiliary TTT task for updating and identifying reliable parameters to update
during the test phase. Both previous arts and our experiments indicate that TTT
may not improve but be detrimental to the learned model if those two factors
are not properly considered. This work addresses those two factors by proposing
an Improved Test-Time Adaptation (ITTA) method. First, instead of heuristically
defining an auxiliary objective, we propose a learnable consistency loss for
the TTT task, which contains learnable parameters that can be adjusted toward
better alignment between our TTT task and the main prediction task. Second, we
introduce additional adaptive parameters for the trained model, and we suggest
only updating the adaptive parameters during the test phase. Through extensive
experiments, we show that the proposed two strategies are beneficial for the
learned model (see Figure 1), and ITTA could achieve superior performance to
the current state-of-the-art methods on several DG benchmarks. Code is
available at this https URL

本文提出了一种改进的测试时间自适应方法（ITTA），通过引入一个可学习的一致性损失，用于更新辅助测试任务，并且只在测试阶段更新可训练参数，从而解决了域泛化中分布偏移问题，实验表明，该方法在多个评估基准上具有卓越的性能。