Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach

• URL: http://arxiv.org/abs/2403.19067v1
• Date: Thu, 28 Mar 2024 00:14:53 GMT
• Title: Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
• Authors: Wei Dong, Xing Zhang, Bihui Chen, Dawei Yan, Zhijun Lin, Qingsen Yan, Peng Wang, Yang Yang,
• Abstract summary: Fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features is a key challenge. We propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy.
• Score: 17.678759882763078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at \href{https://github.com/zstarN70/RLRR.git}{https://github.com/zstarN70/RLRR.git}.

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