AutoFT: Learning an Objective for Robust Fine-Tuning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910367278432256 |
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| author | Choi, Caroline Lee, Yoonho Chen, Annie Zhou, Allan Raghunathan, Aditi Finn, Chelsea |
| author_facet | Choi, Caroline Lee, Yoonho Chen, Annie Zhou, Allan Raghunathan, Aditi Finn, Chelsea |
| contents | Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. However, fine-tuning a model on one data distribution often degrades performance under distribution shifts. Current approaches to robust fine-tuning use hand-crafted regularization techniques to constrain the fine-tuning process towards the pretrained model. Yet, it is hard to specify how to adapt relevant characteristics of the foundation model during fine-tuning, as this depends on how the pre-training, fine-tuning, and test data distributions relate to each other. We propose AutoFT, a data-driven approach for robust fine-tuning. Given a task, AutoFT searches for a fine-tuning procedure that enhances out-of-distribution (OOD) generalization. Specifically, AutoFT uses bi-level optimization to search for an objective function and hyperparameters that maximize post-adaptation performance on a small OOD validation set. We evaluate AutoFT on nine natural distribution shifts. Our experiments show that AutoFT significantly improves generalization to OOD inputs, outperforming existing robust fine-tuning methods. Notably, AutoFT achieves a new state-of-the-art on the WILDS iWildCam and FMoW benchmarks, outperforming the previous best methods by $6.0\%$ and $1.5\%$, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoFT: Learning an Objective for Robust Fine-Tuning Choi, Caroline Lee, Yoonho Chen, Annie Zhou, Allan Raghunathan, Aditi Finn, Chelsea Machine Learning Computer Vision and Pattern Recognition Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. However, fine-tuning a model on one data distribution often degrades performance under distribution shifts. Current approaches to robust fine-tuning use hand-crafted regularization techniques to constrain the fine-tuning process towards the pretrained model. Yet, it is hard to specify how to adapt relevant characteristics of the foundation model during fine-tuning, as this depends on how the pre-training, fine-tuning, and test data distributions relate to each other. We propose AutoFT, a data-driven approach for robust fine-tuning. Given a task, AutoFT searches for a fine-tuning procedure that enhances out-of-distribution (OOD) generalization. Specifically, AutoFT uses bi-level optimization to search for an objective function and hyperparameters that maximize post-adaptation performance on a small OOD validation set. We evaluate AutoFT on nine natural distribution shifts. Our experiments show that AutoFT significantly improves generalization to OOD inputs, outperforming existing robust fine-tuning methods. Notably, AutoFT achieves a new state-of-the-art on the WILDS iWildCam and FMoW benchmarks, outperforming the previous best methods by $6.0\%$ and $1.5\%$, respectively. |
| title | AutoFT: Learning an Objective for Robust Fine-Tuning |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.10220 |