Summary of Unitta: Unified Benchmark and Versatile Framework Towards Realistic Test-time Adaptation, by Chaoqun Du et al.
UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation
by Chaoqun Du, Yulin Wang, Jiayi Guo, Yizeng Han, Jie Zhou, Gao Huang
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a Unified Test-Time Adaptation (UniTTA) benchmark for adapting pre-trained models to target domains during testing. The UniTTA benchmark addresses various challenging scenarios, including continual domain shifts, mixed domains, and temporally correlated or imbalanced class distributions. It considers both domain and class as independent dimensions of data and covers 36 scenarios. Alongside the benchmark, the authors propose a versatile UniTTA framework, which includes a Balanced Domain Normalization (BDN) layer and a COrrelated Feature Adaptation (COFA) method. Extensive experiments demonstrate that the UniTTA framework excels within the UniTTA benchmark and achieves state-of-the-art performance on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at adapting to new situations during testing. Right now, this adaptation can be influenced by many things. The authors created a special tool called UniTTA that helps scientists evaluate how well different methods work in these situations. They also developed a framework that includes two important parts: one that makes the data more balanced and another that adapts to changes in features. The results show that their framework performs better than others in similar situations. |
