UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation
Chaoqun Du, Yulin Wang, Jiayi Guo, Yizeng Han, Jie Zhou, Gao Huang

TL;DR
UniTTA introduces a comprehensive benchmark and a versatile framework for realistic test-time adaptation, addressing diverse domain and class distribution challenges with state-of-the-art results.
Contribution
It provides the first unified benchmark covering 36 scenarios and a novel framework with BDN and COFA methods for effective TTA.
Findings
UniTTA framework outperforms existing methods on the benchmark.
The benchmark covers 36 realistic TTA scenarios.
The proposed methods achieve state-of-the-art results.
Abstract
Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target domain during testing. In reality, this adaptability can be influenced by multiple factors. Researchers have identified various challenging scenarios and developed diverse methods to address these challenges, such as dealing with continual domain shifts, mixed domains, and temporally correlated or imbalanced class distributions. Despite these efforts, a unified and comprehensive benchmark has yet to be established. To this end, we propose a Unified Test-Time Adaptation (UniTTA) benchmark, which is comprehensive and widely applicable. Each scenario within the benchmark is fully described by a Markov state transition matrix for sampling from the original dataset. The UniTTA benchmark considers both domain and class as two independent dimensions of data and addresses various combinations of imbalance/balance and…
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Testing and Debugging Techniques · Educational Technology and Assessment
