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

Source: [Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation](<https://www.sota2.com/research/paper/68fa71b61e209131cea52b36>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:11:33.256Z

Authors: Mario D\\"obler, Florian Marencke, Robert A. Marsden, Bin Yang

Published: 2024-01-02T01:56:25.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.00989>)

[PDF](<https://arxiv.org/pdf/2401.00989.pdf>)