Fully Test-Time Adaptation (TTA), which aims at adapting models to data
drifts, has recently attracted wide interest. Numerous tricks and techniques
have been proposed to ensure robust learning on arbitrary streams of unlabeled
data. However, assessing the true impact of each individual technique and
obtaining a fair comparison still constitutes a significant challenge. To help
consolidate the community's knowledge, we present a categorization of selected
orthogonal TTA techniques, including small batch normalization, stream
rebalancing, reliable sample selection, and network confidence calibration. We
meticulously dissect the effect of each approach on different scenarios of
interest. Through our analysis, we shed light on trade-offs induced by those
techniques between accuracy, the computational power required, and model
complexity. We also uncover the synergy that arises when combining techniques
and are able to establish new state-of-the-art results.

全面测试时间适应 (TTA) 旨在使模型适应数据漂移，最近引起了广泛的兴趣。我们提供了选择的正交 TTA 技术的分类，并对其对不同情景的影响进行了细致分析。我们揭示了这些技术在准确性、计算能力和模型复杂性之间产生的权衡，并发现了将技术结合起来产生的协同效应，从而取得了新的最先进结果。