Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. Current methods predominantly optimize the model for each incoming test data batch using self-training loss. While these methods yield commendable results in ideal test data streams, where batches are independently and identically sampled from the target distribution, they falter under more practical test data streams that are not independent and identically distributed (non-i.i.d.). The data batches in a non-i.i.d. stream display prominent label shifts relative to each other. It leads to conflicting optimization objectives among batches during the TTA process. Given the inherent risks of adapting the source model to unpredictable test-time distributions, we reverse the adaptation process and propose a novel Distribution Alignment loss for TTA. This loss guides the distributions of test-time features back towards the source distributions, which ensures compatibility with the well-trained source model and eliminates the pitfalls associated with conflicting optimization objectives. Moreover, we devise a domain shift detection mechanism to extend the success of our proposed TTA method in the continual domain shift scenarios. Our extensive experiments validate the logic and efficacy of our method. On six benchmark datasets, we surpass existing methods in non-i.i.d. scenarios and maintain competitive performance under the ideal i.i.d. assumption.

给定源数据训练的模型，测试时间自适应（TTA）能够适应带有领域变化的测试数据流，并实现推理。我们提出了一种新的分布对齐损失函数，将测试时间特征的分布引导回源分布，确保与训练充分的源模型兼容并消除冲突的优化目标的问题，同时设计了一种领域转移检测机制，以在持续领域转移场景中提高我们所提出的TTA方法的成功率。通过广泛的实验证实了我们方法的逻辑和有效性，在六个基准数据集上，在非独立同分布的情况下超过现有方法，并在理想独立同分布假设下保持竞争性的性能。

全面测试时间自适应的动态在线数据流分布对齐