Summary of Bridging Domains with Approximately Shared Features, by Ziliang Samuel Zhong et al.
Bridging Domains with Approximately Shared Features
by Ziliang Samuel Zhong, Xiang Pan, Qi Lei
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 The paper proposes a statistical framework for multi-source domain adaptation that distinguishes the utilities of features based on their variance in correlation with the label across domains. The framework learns an approximately shared feature representation from source tasks and fine-tunes it on the target task, outperforming previous results on both source and target tasks. This resolves part of the paradox between learning invariant and diverse features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a problem where machine learning models don’t work well when used in new situations. They suggest a way to find important features that help with this problem. The idea is to learn some common things from different sources and then adjust it for the new situation. This helps make the model better than before. |
Keywords
* Artificial intelligence * Domain adaptation * Machine learning
