Sep 25, 2019 ICLR 2020 Conference Blind Submission readers: everyone Show Bibtex
  • TL;DR: A new divergence family dealing with distributions with different supports for training implicit generative models.
  • Abstract: For distributions $p$ and $q$ with different supports, the divergence $\div{p}{q}$ may 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 demonstrate how to maximize the discriminatory power of a given divergence by parameterizing and learning the spread. We also give examples of using a spread divergence to train and improve implicit generative models, including linear models (Independent Components Analysis) and non-linear models (Deep Generative Networks).
  • Code: https://drive.google.com/file/d/1p6l7J1HpcNTV1RrF12wwCza-98m1J8di/view?usp=sharing
  • Keywords: divergence minimization, generative model, variational inference
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