Summary of Localizing Task Information For Improved Model Merging and Compression, by Ke Wang et al.
Localizing Task Information for Improved Model Merging and Compression
by Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez, François Fleuret, Pascal Frossard
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 paper presents a novel approach to merging multiple single-task checkpoints into a multi-task model, addressing the significant performance loss often observed in previous works. The authors show that despite this loss, the information required to solve each task is still preserved after merging, and propose TALL-masks to identify task-specific features. They also introduce Consensus Merging, an algorithm that eliminates selfish and catastrophic weights detrimental to multi-task fusion. Experimental results on vision and NLP benchmarks demonstrate improved performance and reduced storage requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to combine multiple single-task models into one supermodel. It helps by showing that each task uses different parts of the model, so even when they get mixed up, most information is still there. The authors came up with special masks to find these task-specific parts and propose an algorithm to remove unwanted pieces that can ruin the combined model. Tests on various tasks like image recognition and language processing show that their approach works better than others and saves storage space. |
Keywords
» Artificial intelligence » Multi task » Nlp
