Domain-Aware Fine-Tuning of Foundation Models
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910521131794432 |
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| author | Kaplan, Ugur Ali Keuper, Margret Khoreva, Anna Zhang, Dan Li, Yumeng |
| author_facet | Kaplan, Ugur Ali Keuper, Margret Khoreva, Anna Zhang, Dan Li, Yumeng |
| contents | Foundation models (FMs) have revolutionized computer vision, enabling effective learning across different domains. However, their performance under domain shift is yet underexplored. This paper investigates the zero-shot domain adaptation potential of FMs by comparing different backbone architectures and introducing novel domain-aware components that leverage domain related textual embeddings. We propose domain adaptive normalization, termed as Domino, which explicitly leverages domain embeddings during fine-tuning, thus making the model domain aware. Ultimately, Domino enables more robust computer vision models that can adapt effectively to various unseen domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03482 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain-Aware Fine-Tuning of Foundation Models Kaplan, Ugur Ali Keuper, Margret Khoreva, Anna Zhang, Dan Li, Yumeng Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Foundation models (FMs) have revolutionized computer vision, enabling effective learning across different domains. However, their performance under domain shift is yet underexplored. This paper investigates the zero-shot domain adaptation potential of FMs by comparing different backbone architectures and introducing novel domain-aware components that leverage domain related textual embeddings. We propose domain adaptive normalization, termed as Domino, which explicitly leverages domain embeddings during fine-tuning, thus making the model domain aware. Ultimately, Domino enables more robust computer vision models that can adapt effectively to various unseen domains. |
| title | Domain-Aware Fine-Tuning of Foundation Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.03482 |