Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation

• URL: http://arxiv.org/abs/2401.00989v1
• Date: Tue, 2 Jan 2024 01:56:25 GMT
• Title: Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation
• Authors: Mario D\"obler, Florian Marencke, Robert A. Marsden, Bin Yang
• Abstract summary: Test data streams are not always independent and identically distributed (i.i.d.) We propose a diversity-aware and category-balanced buffer that can simulate an i.i.d. data stream, even in non-i.i.d. scenarios. We achieve state-of-the-art results on most considered benchmarks.
• Score: 3.1265626879839923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model during test-time, leveraging the current test data. In real-world scenarios, test data streams are not always independent and identically distributed (i.i.d.). Instead, they are frequently temporally correlated, making them non-i.i.d. Many existing methods struggle to cope with this scenario. In response, we propose a diversity-aware and category-balanced buffer that can simulate an i.i.d. data stream, even in non-i.i.d. scenarios. Combined with a diversity and entropy-weighted entropy loss, we show that a stable adaptation is possible on a wide range of corruptions and natural domain shifts, based on ImageNet. We achieve state-of-the-art results on most considered benchmarks.

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