论文标题
注意：稳健的持续测试时间适应时间相关性
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
论文作者
论文摘要
测试时间适应（TTA）是一个新兴的范式，可解决培训和测试阶段之间的分布变化，而无需其他数据采集或标记成本；仅使用未标记的测试数据流进行连续模型适应。以前的TTA方案假设测试样本是独立的，并且在应用程序方案中通常在时间上相关（non-i.i.d。），例如自动驾驶。我们发现，在这种情况下，大多数现有的TTA方法急剧失败。在此激励的情况下，我们提出了一种新的测试时间适应方案，该方案对非I.I.D具有强大的态度。测试数据流。我们的新颖性主要是两倍：（a）纠正分布样本的归一化的实例感知批归归量表（IABN），以及（b）模拟I.I.D.的预测均衡储层采样（PBR）。来自非i.i.d的数据流。以班级平衡的方式流式传输。我们对各种数据集的评估，包括现实世界非i.i.d。流，表明所提出的强大TTA不仅优于非i.i.d的最先进的TTA算法。设置，但也可以实现与I.I.D.下的这些算法相当的性能。假设。代码可从https://github.com/taesikgong/note获得。
Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation. Previous TTA schemes assume that the test samples are independent and identically distributed (i.i.d.), even though they are often temporally correlated (non-i.i.d.) in application scenarios, e.g., autonomous driving. We discover that most existing TTA methods fail dramatically under such scenarios. Motivated by this, we present a new test-time adaptation scheme that is robust against non-i.i.d. test data streams. Our novelty is mainly two-fold: (a) Instance-Aware Batch Normalization (IABN) that corrects normalization for out-of-distribution samples, and (b) Prediction-balanced Reservoir Sampling (PBRS) that simulates i.i.d. data stream from non-i.i.d. stream in a class-balanced manner. Our evaluation with various datasets, including real-world non-i.i.d. streams, demonstrates that the proposed robust TTA not only outperforms state-of-the-art TTA algorithms in the non-i.i.d. setting, but also achieves comparable performance to those algorithms under the i.i.d. assumption. Code is available at https://github.com/TaesikGong/NOTE.
