This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., transductive batch normalization (TBN), which calculates the mean and the variance from the current test batch rather than using the running mean and variance obtained from the source data, i.e., conventional batch normalization (CBN). Adopting TBN that employs test batch statistics mitigates the performance degradation caused by the domain shift. However, re-estimating normalization statistics using test data depends on impractical assumptions that a test batch should be large enough and be drawn from i.i.d. stream, and we observed that the previous methods with TBN show critical performance drop without the assumptions. In this paper, we identify that CBN and TBN are in a trade-off relationship and present a new test-time normalization (TTN) method that interpolates the statistics by adjusting the importance between CBN and TBN according to the domain-shift sensitivity of each BN layer. Our proposed TTN improves model robustness to shifted domains across a wide range of batch sizes and in various realistic evaluation scenarios. TTN is widely applicable to other test-time adaptation methods that rely on updating model parameters via backpropagation. We demonstrate that adopting TTN further improves their performance and achieves state-of-the-art performance in various standard benchmarks.

翻译：本文提出了一种面向测试时自适应的新型批量归一化策略。现有测试时自适应方法高度依赖改进型批量归一化（即传导式批量归一化，TBN），该方法使用当前测试批次的均值和方差，而非源数据获得的运行均值和方差（即传统批量归一化，CBN）。采用基于测试批次统计量的TBN可缓解领域偏移导致的性能退化，但基于测试数据重估归一化统计量需依赖测试批次足够大且满足独立同分布流等非实际假设。研究发现，现有方法在违背这些假设时会出现显著性能下降。本文揭示了CBN与TBN存在的权衡关系，并提出一种新型测试时归一化方法（TTN），该方法通过根据各BN层对领域偏移的敏感度动态调节CBN与TBN的权重，实现统计量的插值融合。所提TTN能提升模型在各类实际评估场景中跨度批次大小的领域偏移鲁棒性，且可广泛适用于依赖反向传播更新模型参数的其他测试时自适应方法。实验表明，采用TTN能进一步提升此类方法的性能，并在多项标准基准测试中达到最优结果。