Summary of Spectrum-aware Parameter Efficient Fine-tuning For Diffusion Models, by Xinxi Zhang et al.
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models
by Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu, Akash Srivastava, Junzhou Huang, Hao Wang, Molei Tao, Dimitris N. Metaxas
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel framework for adapting large-scale generative models in a parameter-efficient manner. The method, called Spectral Orthogonal Decomposition Adaptation (SODA), adjusts both singular values and their basis vectors of pre-trained weights using the Kronecker product and efficient Stiefel optimizers. This approach balances computational efficiency and representation capacity, making it an attractive alternative to existing fine-tuning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adapting big computer models so they can do new tasks is important. Right now, there are ways to make this happen, but they might not be the best for certain jobs that need a lot of information. This paper introduces a new way to adapt these models, called SODA. It works by changing both the strength and direction of the connections between the model’s parts. This helps it do new tasks better while still being efficient with its computer power. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
