Summary of Learning Scalable Model Soup on a Single Gpu: An Efficient Subspace Training Strategy, by Tao Li et al.
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy
by Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces Memory Efficient Hyperplane Learned Soup (MEHL-Soup), a novel approach to model soups that addresses the limitations of previous methods. By formulating the learned soup as a hyperplane optimization problem, MEHL-Soup reduces memory and time costs by only loading a few fine-tuned models at each iteration. The authors extend MEHL-Soup to MEHL-Soup+ in a layer-wise manner, achieving improved performance on various ViT models and data sets. Compared to Learned-Soup+, MEHL-Soup+ outperforms it in terms of test accuracy while reducing memory usage by over 13 times. Furthermore, MEHL-Soup+ can be run on a single GPU, achieving a 9-fold speedup in soup construction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers learn better and faster. Currently, people use something called “model soups” to help machines learn new tasks. The problem with this approach is that it uses too much memory and takes a long time to work. To solve this issue, the researchers created a new method called MEHL-Soup (Memory Efficient Hyperplane Learned Soup). It works by only loading a few important models at a time, which makes it faster and uses less memory. The results show that this new method is better than previous ones in learning new tasks and takes up less space on computers. |
Keywords
* Artificial intelligence * Optimization * Vit
