Summary of More Is Better: Deep Domain Adaptation with Multiple Sources, by Sicheng Zhao et al.
More is Better: Deep Domain Adaptation with Multiple Sources
by Sicheng Zhao, Hui Chen, Hu Huang, Pengfei Xu, Guiguang Ding
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 presents a survey on multi-source domain adaptation (MDA), which aims to transfer learned knowledge from multiple labeled sources to an unlabeled or sparsely labeled target domain. The authors define various MDA strategies and systematically compare modern MDA methods in the deep learning era from different perspectives, using commonly used datasets and benchmarks. The paper also discusses future research directions for MDA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are trying to find a way to use what they’ve learned from one group of labeled data to make predictions on another group that isn’t as well-labeled. They’re looking at ways to “translate” the knowledge from multiple sources to a new target domain. The authors are giving an overview of how different methods work and which ones perform best, using datasets and benchmarks to test their ideas. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation
