Summary of Multi-source Unsupervised Domain Adaptation with Prototype Aggregation, by Min Huang et al.
Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation
by Min Huang, Zifeng Xie, Bo Sun, Ning Wang
First submitted to arxiv on: 20 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 This paper addresses challenges in multi-source domain adaptation (MSDA) by proposing a prototype aggregation method that models discrepancies between source and target domains at both class and domain levels. The approach uses group prototypes to achieve domain adaptation, with a similarity score-based strategy for quantifying transferability of each domain. Class-specific cross-domain discrepancy is also measured using reliable target pseudo-labels. The results demonstrate state-of-the-art performance on three benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by fixing problems in how they adapt to new situations. It’s like teaching a machine to understand different ways of speaking or writing, so it can make good decisions. The researchers propose a new way of doing this that works better than other methods. They test their idea on three sets of data and show that it does a great job. |
Keywords
» Artificial intelligence » Domain adaptation » Transferability
