Summary of Spectral Adapter: Fine-tuning in Spectral Space, by Fangzhao Zhang et al.
Spectral Adapter: Fine-Tuning in Spectral Space
by Fangzhao Zhang, Mert Pilanci
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors investigate the enhancement of Parameter-Efficient Fine-Tuning (PEFT) methods by incorporating spectral information from pretrained weight matrices. They propose two novel spectral adaptation mechanisms, additive tuning and orthogonal rotation, which improve the rank capacity of low-rank adapters given a fixed parameter budget. Theoretical analysis shows that this approach enhances fine-tuning performance. Experimental results demonstrate better parameter efficiency, tuning performance, and benefits for multi-adapter fusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how to make fine-tuning deep neural networks more efficient by using the spectral information from their starting weights. The researchers try two new ways to adapt this information: adding it directly or rotating it with other important components. This helps improve the ability of adapters to handle low-rank data while keeping the number of learnable parameters small. Experimental results show that this approach leads to better performance and efficiency. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
