Summary of Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates, by Shirley Kokane et al.
Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates
by Shirley Kokane, Mostofa Rafid Uddin, Min Xu
First submitted to arxiv on: 5 Jul 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 This paper proposes a novel layer-wise learning scheme to improve transfer learning methods’ performance in complex tasks. Unlike traditional approaches, which calculate cumulative differences and back-propagate loss through all layers, this method adjusts learning parameters per layer based on Jacobian/Attention/Hessian differences. The authors apply this scheme to attention map-based and derivative-based (first and second order) transfer learning methods, achieving improved learning performance and stability across various datasets. Experimental results show that the performance boost is more significant with increasing task difficulty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new way to improve how computers learn from previous experiences. Right now, most computer models start doing poorly when they have to learn something really hard. The researchers in this paper came up with a new idea: instead of looking at all the features at once, look at each layer separately and adjust how you learn based on what’s changing. They tested this method on different kinds of data and found that it made learning more stable and accurate. The results show that when tasks get harder, this new way helps even more. |
Keywords
» Artificial intelligence » Attention » Transfer learning
