Summary of Trustworthy Transfer Learning: a Survey, by Jun Wu and Jingrui He
Trustworthy Transfer Learning: A Survey
by Jun Wu, Jingrui He
First submitted to arxiv on: 18 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 explore transfer learning from two perspectives: knowledge transferability and trustworthiness. They investigate how to quantify and enhance knowledge transfer across domains, as well as whether transferred knowledge can be trusted. To answer these questions, the authors provide a comprehensive review of trustworthy transfer learning, covering problem definitions, theoretical analysis, empirical algorithms, and real-world applications. The paper discusses recent theories and algorithms for understanding knowledge transferability under both IID and non-IID assumptions. Additionally, it reviews the impact of trustworthiness on transfer learning, including adversarial robustness, algorithmic fairness, privacy-preserving constraints, and more. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using what we already know to help machines learn new things. Researchers want to figure out how to share knowledge between different areas of expertise. They’re also trying to understand if the information being shared is trustworthy or not. The authors look at recent ideas and techniques for sharing knowledge, as well as how this process can be made more reliable. |
Keywords
» Artificial intelligence » Transfer learning » Transferability
