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Partial Distribution Matching via Partial Wasserstein Adversarial Networks
This paper studies the problem of distribution matching (DM), which is a fundamental machine learning problem seeking to robustly align two probability distributions.
Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration
To handle large point sets, we propose a scalable PDM algorithm by utilizing the efficient partial Wasserstein-1 (PW) discrepancy.
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