Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
<|reference_start|>Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach: 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}.<|reference_end|>
@article{dong2024low-rank,
    title={Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design
  Approach},
    author={Wei Dong, Xing Zhang, Bihui Chen, Dawei Yan, Zhijun Lin, Qingsen Yan,
  Peng Wang, Yang Yang},
    journal={arXiv preprint arXiv:2403.19067},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19067},
    primaryClass={cs.CV}
}