DATTA: Domain Diversity Aware Test-Time Adaptation for Dynamic Domain Shift Data Streams
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917167722659840 |
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| author | Ye, Chuyang Wei, Dongyan Liu, Zhendong Pang, Yuanyi Lin, Yixi Jiang, Qinting Jiang, Jingyan He, Dongbiao |
| author_facet | Ye, Chuyang Wei, Dongyan Liu, Zhendong Pang, Yuanyi Lin, Yixi Jiang, Qinting Jiang, Jingyan He, Dongbiao |
| contents | Test-Time Adaptation (TTA) addresses domain shifts between training and testing. However, existing methods assume a homogeneous target domain (e.g., single domain) at any given time. They fail to handle the dynamic nature of real-world data, where single-domain and multiple-domain distributions change over time. We identify that performance drops in multiple-domain scenarios are caused by batch normalization errors and gradient conflicts, which hinder adaptation. To solve these challenges, we propose Domain Diversity Adaptive Test-Time Adaptation (DATTA), the first approach to handle TTA under dynamic domain shift data streams. It is guided by a novel domain-diversity score. DATTA has three key components: a domain-diversity discriminator to recognize single- and multiple-domain patterns, domain-diversity adaptive batch normalization to combine source and test-time statistics, and domain-diversity adaptive fine-tuning to resolve gradient conflicts. Extensive experiments show that DATTA significantly outperforms state-of-the-art methods by up to 13%. Code is available at https://github.com/DYW77/DATTA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08056 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DATTA: Domain Diversity Aware Test-Time Adaptation for Dynamic Domain Shift Data Streams Ye, Chuyang Wei, Dongyan Liu, Zhendong Pang, Yuanyi Lin, Yixi Jiang, Qinting Jiang, Jingyan He, Dongbiao Machine Learning Test-Time Adaptation (TTA) addresses domain shifts between training and testing. However, existing methods assume a homogeneous target domain (e.g., single domain) at any given time. They fail to handle the dynamic nature of real-world data, where single-domain and multiple-domain distributions change over time. We identify that performance drops in multiple-domain scenarios are caused by batch normalization errors and gradient conflicts, which hinder adaptation. To solve these challenges, we propose Domain Diversity Adaptive Test-Time Adaptation (DATTA), the first approach to handle TTA under dynamic domain shift data streams. It is guided by a novel domain-diversity score. DATTA has three key components: a domain-diversity discriminator to recognize single- and multiple-domain patterns, domain-diversity adaptive batch normalization to combine source and test-time statistics, and domain-diversity adaptive fine-tuning to resolve gradient conflicts. Extensive experiments show that DATTA significantly outperforms state-of-the-art methods by up to 13%. Code is available at https://github.com/DYW77/DATTA. |
| title | DATTA: Domain Diversity Aware Test-Time Adaptation for Dynamic Domain Shift Data Streams |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.08056 |