Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation

• URL: http://arxiv.org/abs/2401.08328v2
• Date: Thu, 14 Mar 2024 11:20:21 GMT
• Title: Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation
• Authors: Devavrat Tomar, Guillaume Vray, Jean-Philippe Thiran, Behzad Bozorgtabar,
• Abstract summary: This paper presents a novel method termed 'Un-Mixing Test-Time Normalization Statistics' (UnMix-TNS) Our method re-calibrates the statistics for each instance within a test batch by mixing it with multiple distinct statistics components. Our results highlight UnMix-TNS's capacity to markedly enhance stability and performance across various benchmarks.
• Score: 11.743315123714108
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent test-time adaptation methods heavily rely on nuanced adjustments of batch normalization (BN) parameters. However, one critical assumption often goes overlooked: that of independently and identically distributed (i.i.d.) test batches with respect to unknown labels. This oversight leads to skewed BN statistics and undermines the reliability of the model under non-i.i.d. scenarios. To tackle this challenge, this paper presents a novel method termed 'Un-Mixing Test-Time Normalization Statistics' (UnMix-TNS). Our method re-calibrates the statistics for each instance within a test batch by mixing it with multiple distinct statistics components, thus inherently simulating the i.i.d. scenario. The core of this method hinges on a distinctive online unmixing procedure that continuously updates these statistics components by incorporating the most similar instances from new test batches. Remarkably generic in its design, UnMix-TNS seamlessly integrates with a wide range of leading test-time adaptation methods and pre-trained architectures equipped with BN layers. Empirical evaluations corroborate the robustness of UnMix-TNS under varied scenarios-ranging from single to continual and mixed domain shifts, particularly excelling with temporally correlated test data and corrupted non-i.i.d. real-world streams. This adaptability is maintained even with very small batch sizes or single instances. Our results highlight UnMix-TNS's capacity to markedly enhance stability and performance across various benchmarks. Our code is publicly available at https://github.com/devavratTomar/unmixtns.

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