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arXiv · 1606.04218

Conditional Generative Moment-Matching Networks

Yong Ren·Jialian Li·Yucen Luo·Jun Zhu
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Abstract

Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this paper, we present conditional generative moment- matching networks (CGMMN), which learn a conditional distribution given some input variables based on a conditional maximum mean discrepancy (CMMD) criterion. The learning is performed by stochastic gradient descent with the gradient calculated by back-propagation. We evaluate CGMMN on a wide range of tasks, including predictive modeling, contextual generation, and Bayesian dark knowledge, which distills knowledge from a Bayesian model by learning a relatively small CGMMN student network. Our results demonstrate competitive performance in all the tasks.

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Yong Ren, Jialian Li, Yucen Luo, Jun Zhu. 2016-06-14. Conditional Generative Moment-Matching Networks. https://arxiv.org/abs/1606.04218

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