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energy-based sliced wasserstein distance

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Energy-Based Sliced Wasserstein Distance

Neural Information Processing SystemsDec-24-2025, 16:52:59 GMT

The sliced Wasserstein (SW) distance has been widely recognized as a statistically effective and computationally efficient metric between two probability measures. A key component of the SW distance is the slicing distribution. There are two existing approaches for choosing this distribution. The first approach is using a fixed prior distribution. The second approach is optimizing for the best distribution which belongs to a parametric family of distributions and can maximize the expected distance.

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Neural Information Processing Systems

Technology: Information Technology > Artificial Intelligence > Machine Learning (0.56)

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