Current test-time adaptation (TTA) approaches aim to adapt to environments that change continuously. Yet, when the environments not only change but also recur in a correlated manner over time, such as in the case of day-night surveillance cameras, it is unclear whether the adaptability of these methods is sustained after a long run. This study aims to examine the error accumulation of TTA models when they are repeatedly exposed to previous testing environments, proposing a novel testing setting called episodic TTA. To study this phenomenon, we design a simulation of TTA process on a simple yet representative $\epsilon$-perturbed Gaussian Mixture Model Classifier and derive the theoretical findings revealing the dataset- and algorithm-dependent factors that contribute to the gradual degeneration of TTA methods through time. Our investigation has led us to propose a method, named persistent TTA (PeTTA). PeTTA senses the model divergence towards a collapsing and adjusts the adaptation strategy of TTA, striking a balance between two primary objectives: adaptation and preventing model collapse. The stability of PeTTA in the face of episodic TTA scenarios has been demonstrated through a set of comprehensive experiments on various benchmarks.

翻译：当前测试时适应（TTA）方法旨在适应持续变化的环境。然而，当环境不仅变化，而且随时间呈现相关性重复时（如昼夜监控摄像头的场景），这些方法的适应性在长期运行后是否能够持续仍不明确。本研究旨在探究TTA模型在反复暴露于先前测试环境时的误差累积现象，并提出一种名为"情境化测试时适应"（episodic TTA）的新测试场景。为研究该现象，我们基于一个简洁且具代表性的$\epsilon$扰动高斯混合模型分类器设计了TTA过程仿真，并从理论上揭示了导致TTA方法随时间逐渐退化的数据集相关与算法相关因素。基于此研究，我们提出了一种名为"持续性测试时适应"（PeTTA）的方法。PeTTA通过感知模型向崩溃方向的发散程度，动态调整TTA的适应策略，在适应性与防止模型崩溃这两个主要目标间取得平衡。通过一系列综合实验，我们在多个基准测试上验证了PeTTA在情境化测试场景中的稳定性。