Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models

Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing HONG, Zhenguo Li, Ricardo Silva, Nevin L. Zhang

Keywords: robustness, fine-tuning zero-shot models, CLIP, concept descriptions

TL;DR: Combining empirical and worst-case risk minimization enhances the robustness of fine-tuned CLIP models.

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$\to$77.1), WILDS-iWildCam (47.1$\to$51.8), and WILDS-FMoW (50.7$\to$53.1); opening up new avenues for robust fine-tuning. Our code is available at https://github.com/vaynexie/DRM.

Primary Area: Optimization for deep networks

Submission Number: 4