Spectral Adapter: Fine-Tuning in Spectral Space
This work addresses the need for more efficient fine-tuning methods in machine learning, offering incremental improvements over existing PEFT approaches.
The paper tackles the problem of improving parameter-efficient fine-tuning (PEFT) for pretrained deep neural networks by incorporating spectral information from weight matrices, resulting in enhanced rank capacity, better parameter efficiency, and improved tuning performance with benefits for multi-adapter fusion.
Recent developments in Parameter-Efficient Fine-Tuning (PEFT) methods for pretrained deep neural networks have captured widespread interest. In this work, we study the enhancement of current PEFT methods by incorporating the spectral information of pretrained weight matrices into the fine-tuning procedure. We investigate two spectral adaptation mechanisms, namely additive tuning and orthogonal rotation of the top singular vectors, both are done via first carrying out Singular Value Decomposition (SVD) of pretrained weights and then fine-tuning the top spectral space. We provide a theoretical analysis of spectral fine-tuning and show that our approach improves the rank capacity of low-rank adapters given a fixed trainable parameter budget. We show through extensive experiments that the proposed fine-tuning model enables better parameter efficiency and tuning performance as well as benefits multi-adapter fusion.