Summary of Expanding Sparse Tuning For Low Memory Usage, by Shufan Shen et al.
Expanding Sparse Tuning for Low Memory Usage
by Shufan Shen, Junshu Sun, Xiangyang Ji, Qingming Huang, Shuhui Wang
First submitted to arxiv on: 4 Nov 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 The proposed SNELL method achieves state-of-the-art performance in adapting pre-trained vision models to downstream tasks while reducing memory usage. Building upon sparse tuning, which selectively adjusts weights most relevant to the task, SNELL decomposes tunable matrices into learnable low-rank matrices, eliminating the need for storing entire weight matrices. Additionally, a competition-based sparsification mechanism avoids storing tunable weight indexes. By extending this approach with nonlinear kernel functions, SNELL enhances its ability to adapt models to tasks while maintaining low memory usage. Experimental results demonstrate SNELL’s effectiveness on multiple downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SNELL is a new way to fine-tune pre-trained vision models for specific tasks without using too much memory. It does this by splitting the important parts of the model into smaller pieces that can be learned and stored efficiently. This approach helps reduce the amount of memory needed while still achieving good results. SNELL also has a special mechanism to avoid storing extra information, which makes it even more efficient. |
