Summary of Reimagining Linear Probing: Kolmogorov-arnold Networks in Transfer Learning, by Sheng Shen and Rabih Younes
Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning
by Sheng Shen, Rabih Younes
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to transfer learning is introduced, enhancing the traditional linear probing method with Kolmogorov-Arnold Networks (KAN). The limitation of linear probing in modeling complex relationships is addressed by substituting it with KAN, which leverages spline-based representations. A ResNet-50 model pre-trained on ImageNet is integrated with KAN and evaluated on the CIFAR-10 dataset. Systematic hyperparameter search optimizes KAN’s flexibility and accuracy, demonstrating significant improvements over traditional linear probing in terms of accuracy and generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to improve transfer learning. It uses something called Kolmogorov-Arnold Networks (KAN) instead of the usual method that works pretty well but has limitations. The old method is good at simple things, but it’s not very good at understanding complex relationships in data. KAN helps with this by using special kinds of functions to understand these relationships better. The researchers tested KAN on a computer vision task and found that it did much better than the usual way. |
Keywords
» Artificial intelligence » Generalization » Hyperparameter » Resnet » Transfer learning
