Summary of Kite: a Kernel-based Improved Transferability Estimation Method, by Yunhui Guo
KITE: A Kernel-based Improved Transferability Estimation Method
by Yunhui Guo
First submitted to arxiv on: 1 May 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 A novel perspective is introduced in this paper to tackle the problem of transferability estimation in transfer learning. The proposed method, called Kite, uses kernel-based techniques to analyze pre-trained models and estimate their ability to deliver good performance on target datasets. The authors argue that two key factors for estimating transferability are the separability of pre-trained features and their similarity to random features. They develop a centered kernel alignment approach to assess these factors and demonstrate the effectiveness of Kite through extensive experiments on a large-scale model selection benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transfer learning is a way to use what we’ve learned from one task to do better on another task. The problem is, we need to figure out which pre-trained models will work best for each new task. This paper presents a new approach called Kite that helps us make this decision. It’s based on the idea that good pre-trained models should have features that are easy to tell apart and similar to random patterns. Kite uses a special kind of math called kernel methods to measure these features. The results show that Kite is much better than other methods at guessing which pre-trained model will work best. |
Keywords
» Artificial intelligence » Alignment » Transfer learning » Transferability
