Summary of Assessing Pre-trained Models For Transfer Learning Through Distribution Of Spectral Components, by Tengxue Zhang et al.
Assessing Pre-Trained Models for Transfer Learning Through Distribution of Spectral Components
by Tengxue Zhang, Yang Shu, Xinyang Chen, Yifei Long, Chenjuan Guo, Bin Yang
First submitted to arxiv on: 26 Dec 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 In this paper, researchers propose a novel approach to assessing pre-trained models for transfer learning. The existing methods focus on analyzing the features extracted by each pre-trained model or how well these features fit the target labels. Instead, the authors introduce the Distribution of Spectral Components (DISCO) framework that decomposes the features into singular values and investigates their contribution to fine-tuning performance. They develop an assessment method based on the distribution of spectral components, which measures the proportions of corresponding singular values. The proposed approach is flexible and can be applied to both classification and regression tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the best pre-trained model for a specific task without spending too much time fine-tuning it. It’s like finding the right tool for the job! By looking at how different parts of the model work together, we can choose the one that will perform well in our situation. The authors tested their method on several tasks and datasets and showed that it produces great results. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Regression » Transfer learning
