Summary of Deep Transfer Learning: Model Framework and Error Analysis, by Yuling Jiao et al.
Deep Transfer Learning: Model Framework and Error Analysis
by Yuling Jiao, Huazhen Lin, Yuchen Luo, Jerry Zhijian Yang
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 framework for deep transfer learning that leverages information from multi-domain upstream data to enhance performance on a single downstream task. The framework allows for both shared and domain-specific features across the upstream domains, enabling automatic identification and precise transfer of relevant information. Additionally, it provides explicit relationships between upstream domains and downstream tasks, enhancing interpretability. Empirical experiments on image classification and regression datasets demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use old data to learn new things. Imagine you have lots of pictures of different types (cats, dogs, cars) and you want to recognize certain objects in those pictures. This framework helps you take advantage of all that old data to make your picture-recognizing model better. It does this by finding the important features in each type of picture and using them to help with recognizing new objects. This makes it easier for computers to learn new things from small amounts of new data. The results show that this approach can be very effective. |
Keywords
» Artificial intelligence » Image classification » Regression » Transfer learning
