We present a simple and effective way to estimate the batch-norm statistics during test time, to fast adapt a source model to target test samples. Known as Test-Time Adaptation, most prior works studying this task follow two assumptions in their evaluation where (1) test samples come together as a large batch, and (2) all from a single test distribution. However, in practice, these two assumptions may not stand, the reasons for which we propose two new evaluation settings where batch sizes are arbitrary and multiple distributions are considered. Unlike the previous methods that require a large batch of single distribution during test time to calculate stable batch-norm statistics, our method avoid any dependency on large online batches and is able to estimate accurate batch-norm statistics with a single sample. The proposed method significantly outperforms the State-Of-The-Art in the newly proposed settings in Test-Time Adaptation Task, and also demonstrates improvements in various other settings such as Source-Free Unsupervised Domain Adaptation and Zero-Shot Classification.

本文提出了一种简单而有效的方法，可以在测试期间估算批量标准化统计量以快速适应目标测试样本，并提出两种新的评估设置，避免对大量在线批次的依赖并能够仅通过单个样本估算准确的批量标准化统计量。该方法在测试时间自适应任务的新提出的设置中明显优于现有方法，并且在其他设置中也有所改进。

MixNorm: 通过在线归一化估计进行测试时适应