Learning with a Wasserstein Loss

@inproceedings{Frogner2015LearningWA,
  title={Learning with a Wasserstein Loss},
  author={Charlie Frogner and Chiyuan Zhang and Hossein Mobahi and Mauricio Araya-Polo and Tomaso A. Poggio},
  booktitle={NIPS},
  year={2015}
}
Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance. The Wasserstein distance provides a natural notion of dissimilarity for probability measures. Although optimizing with respect to the exact Wasserstein distance is costly, recent work has described a regularized approximation that is… CONTINUE READING
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