SNAP: Low-Latency Test-Time Adaptation with Sparse Updates

• URL: http://arxiv.org/abs/2511.15276v1
• Date: Wed, 19 Nov 2025 09:40:24 GMT
• Title: SNAP: Low-Latency Test-Time Adaptation with Sparse Updates
• Authors: Hyeongheon Cha, Dong Min Kim, Hye Won Chung, Taesik Gong, Sung-Ju Lee,
• Abstract summary: Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts.<n>We propose SNAP, a sparse TTA framework that reduces adaptation frequency and data usage while preserving accuracy.<n> SNAP maintains competitive accuracy even when adapting based on only 1% of the incoming data stream.
• Score: 31.155455736641116
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts. However, existing methods rely on frequent adaptation and high computational cost, making them unsuitable for resource-constrained edge environments. To address this, we propose SNAP, a sparse TTA framework that reduces adaptation frequency and data usage while preserving accuracy. SNAP maintains competitive accuracy even when adapting based on only 1% of the incoming data stream, demonstrating its robustness under infrequent updates. Our method introduces two key components: (i) Class and Domain Representative Memory (CnDRM), which identifies and stores a small set of samples that are representative of both class and domain characteristics to support efficient adaptation with limited data; and (ii) Inference-only Batch-aware Memory Normalization (IoBMN), which dynamically adjusts normalization statistics at inference time by leveraging these representative samples, enabling efficient alignment to shifting target domains. Integrated with five state-of-the-art TTA algorithms, SNAP reduces latency by up to 93.12%, while keeping the accuracy drop below 3.3%, even across adaptation rates ranging from 1% to 50%. This demonstrates its strong potential for practical use on edge devices serving latency-sensitive applications. The source code is available at https://github.com/chahh9808/SNAP.

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