Fast Trainable Projection for Robust Fine-Tuning

• URL: http://arxiv.org/abs/2310.19182v1
• Date: Sun, 29 Oct 2023 22:52:43 GMT
• Title: Fast Trainable Projection for Robust Fine-Tuning
• Authors: Junjiao Tian, Yen-Cheng Liu, James Seale Smith, Zsolt Kira
• Abstract summary: Robust fine-tuning aims to achieve competitive in-distribution (ID) performance. Projection-based fine-tuning has been successfully used in robust fine-tuning. Fast Trainable Projection is a new projection-based fine-tuning algorithm.
• Score: 36.51660287722338
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring it to a downstream task. Recently, projected gradient descent has been successfully used in robust fine-tuning by constraining the deviation from the initialization of the fine-tuned model explicitly through projection. However, algorithmically, two limitations prevent this method from being adopted more widely, scalability and efficiency. In this paper, we propose a new projection-based fine-tuning algorithm, Fast Trainable Projection (FTP) for computationally efficient learning of per-layer projection constraints, resulting in an average $35\%$ speedup on our benchmarks compared to prior works. FTP can be combined with existing optimizers such as AdamW, and be used in a plug-and-play fashion. Finally, we show that FTP is a special instance of hyper-optimizers that tune the hyper-parameters of optimizers in a learnable manner through nested differentiation. Empirically, we show superior robustness on OOD datasets, including domain shifts and natural corruptions, across four different vision tasks with five different pre-trained models. Additionally, we demonstrate that FTP is broadly applicable and beneficial to other learning scenarios such as low-label and continual learning settings thanks to its easy adaptability. The code will be available at https://github.com/GT-RIPL/FTP.git.

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