Li, Kaican;
Xie, Weiyan;
Huang, Yongxiang;
Deng, Didan;
Hong, Lanqing;
Li, Zhenguo;
Silva, Ricardo;
(2024)
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models.
In:
Proceedings of NeurIPS 2024: Thirty-Eighth Annual Conference on Neural Information Processing Systems.
(pp. pp. 1-33).
NeurIPS: San Diego, CA, USA.
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Abstract
Fine-tuning foundation models often compromises their robustness to distribution shifts. To remedy this, most robust fine-tuning methods aim to preserve the pre-trained features. However, not all pre-trained features are robust and those methods are largely indifferent to which ones to preserve. We propose dual risk minimization (DRM), which combines empirical risk minimization with worst-case risk minimization, to better preserve the core features of downstream tasks. In particular, we utilize core-feature descriptions generated by LLMs to induce core-based zero-shot predictions which then serve as proxies to estimate the worst-case risk. DRM balances two crucial aspects of model robustness: expected performance and worst-case performance, establishing a new state of the art on various real-world benchmarks. DRM significantly improves the out-of-distribution performance of CLIP ViT-L/14@336 on ImageNet (75.9 → 77.1), WILDS-iWildCam (47.1 → 51.8), and WILDS-FMoW (50.7 → 53.1); opening up new avenues for robust fine-tuning. Our code is available at https://github.com/vaynexie/DRM.
Type: | Proceedings paper |
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Title: | Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models |
Event: | NeurIPS 2024: Thirty-Eighth Annual Conference on Neural Information Processing Systems |
ISBN-13: | 9798331314385 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://proceedings.neurips.cc/paper_files/paper/2... |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10199062 |
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