Summary of Lines: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging, by Ke Wang et al.
LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
by Ke Wang, Nikolaos Dimitriadis, Alessandro Favero, Guillermo Ortiz-Jimenez, Francois Fleuret, Pascal Frossard
First submitted to arxiv on: 22 Oct 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 proposed LiNeS (Layer-increasing Network Scaling) technique addresses the challenges of fine-tuning pre-trained models by preserving their generalization capabilities while enhancing task-specific performance. The method scales parameter updates linearly based on layer depth, allowing shallow layers to retain pre-trained values and deeper layers to adapt to new tasks. This approach demonstrates significant improvements in single-task and multi-task settings across various benchmarks in vision and natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LiNeS is a technique that helps computers learn new things without forgetting what they already know. It’s like a way to keep the computer’s “memory” clear while it learns new skills. The method works by adjusting how much each layer of the computer’s network changes when it’s learning something new. This helps the computer remember important details from before, while still being able to learn new things. LiNeS is tested on many different tasks and shows that it can really help computers generalize better. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Multi task » Natural language processing
