ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains

Guillaume Vray; Devavrat Tomar; Xufeng Gao; Jean-Philippe Thiran; Evan Shelhamer; Behzad Bozorgtabar

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arXiv · 2505.14511

ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains

Guillaume Vray·Devavrat Tomar·Xufeng Gao·Jean-Philippe Thiran·Evan Shelhamer·Behzad Bozorgtabar

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Abstract

This paper introduces ReservoirTTA, a novel plug-in framework designed for prolonged test-time adaptation (TTA) in scenarios where the test domain continuously shifts over time, including cases where domains recur or evolve gradually. At its core, ReservoirTTA maintains a reservoir of domain-specialized models -- an adaptive test-time model ensemble -- that both detects new domains via online clustering over style features of incoming samples and routes each sample to the appropriate specialized model, and thereby enables domain-specific adaptation. This multi-model strategy overcomes key limitations of single model adaptation, such as catastrophic forgetting, inter-domain interference, and error accumulation, ensuring robust and stable performance on sustained non-stationary test distributions. Our theoretical analysis reveals key components that bound parameter variance and prevent model collapse, while our plug-in TTA module mitigates catastrophic forgetting of previously encountered domains. Extensive experiments on scene-level corruption benchmarks (ImageNet-C, CIFAR-10/100-C), object-level style shifts (DomainNet-126, PACS), and semantic segmentation (Cityscapes->ACDC) covering recurring and continuously evolving domain shifts -- show that ReservoirTTA substantially improves adaptation accuracy and maintains stable performance across prolonged, recurring shifts, outperforming state-of-the-art methods. Our code is publicly available at https://github.com/LTS5/ReservoirTTA.

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BibTeX RIS

Guillaume Vray, Devavrat Tomar, Xufeng Gao, Jean-Philippe Thiran, Evan Shelhamer, Behzad Bozorgtabar. 2025-09-18. ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains. https://arxiv.org/abs/2505.14511

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