Summary of Foldable Supernets: Scalable Merging Of Transformers with Different Initializations and Tasks, by Edan Kinderman et al.
Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks
by Edan Kinderman, Itay Hubara, Haggai Maron, Daniel Soudry
First submitted to arxiv on: 2 Oct 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 research paper introduces Foldable SuperNet Merge (FS-Merge), a novel method for merging large transformers trained on different tasks from distinct initializations. Traditional methods fail when applied to this challenging setup, so the authors propose a feature reconstruction loss-based approach to optimize a SuperNet and fuse the original models. FS-Merge is shown to be simple, data-efficient, and capable of merging models of varying widths, outperforming existing methods including knowledge distillation in various settings, sizes, tasks, and modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to combine artificial intelligence models that were trained for different purposes. Currently, most methods can only do this if the models started with the same information, but new research tries to overcome this limitation. The authors introduce a new method called FS-Merge, which works by rebuilding a bigger model using pieces from each of the smaller models. This approach is shown to be better than existing methods at combining models that were trained on different data and tasks. |
Keywords
» Artificial intelligence » Knowledge distillation
