Spread Divergences

David Barber, Mingtian Zhang, Raza Habib, Thomas Bird

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: For distributions $p$ and $q$ with different support, the divergence $\div{p}{q}$ generally will not exist. We define a spread divergence $\sdiv{p}{q}$ on modified $p$ and $q$ and describe sufficient conditions for the existence of such a divergence. We give examples of using a spread divergence to train implicit generative models, including linear models (Principal Components Analysis and Independent Components Analysis) and non-linear models (Deep Generative Networks).
  • Keywords: Generative Adversarial Network, Divergence
  • TL;DR: Using noise to define the divergence between distributions with different support.
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