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It's concerned with the methodologies of Wasserstein distance calculation.

There are some ways to calculate Wasserstein distance, such as

  • Sinkhorn iteration aided method [1],
  • Neural networks applied method (proposed in WGANs[2]), etc.

As the original Optimal Transport is too hard to solve, those methodologies get feasible thanks for approximations. In this way, the efficiency, the accuracy and the trade-off between them do matter.

  • Which one is the most reasonable way?
  • Is there any paper which has already investigated such an issue?
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