Summary of Partial Distribution Matching Via Partial Wasserstein Adversarial Networks, by Zi-ming Wang et al.
Partial Distribution Matching via Partial Wasserstein Adversarial Networks
by Zi-Ming Wang, Nan Xue, Ling Lei, Rebecka Jörnsten, Gui-Song Xia
First submitted to arxiv on: 16 Sep 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 The paper studies distribution matching, a fundamental machine learning problem seeking to align two probability distributions. The authors propose partial distribution matching (PDM), which seeks to match a fraction of the distributions instead of matching them completely. They derive the Kantorovich-Rubinstein duality for the partial Wasserstein-1 discrepancy and develop a partial Wasserstein adversarial network (PWAN) that approximates this discrepancy. The authors demonstrate the effectiveness of their approach on two practical tasks: point set registration and partial domain adaptation. Their results show that the proposed PWAN produces highly robust matching results, comparable to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a big problem in machine learning called distribution matching. It’s like trying to match two puzzles together, but instead of pictures, it’s probability distributions. The authors have a new way to do this, which they call partial distribution matching (PDM). They show that their method works well on two real-world tasks: aligning 3D shapes and adapting computer vision systems for different datasets. |
Keywords
» Artificial intelligence » Domain adaptation » Machine learning » Probability
