Variational Generative Stochastic Networks with Collaborative Shaping

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Authors Philip Bachman, Doina Precup
Journal/Conference Name 32nd International Conference on Machine Learning, ICML 2015
Paper Category
Paper Abstract We develop an approach to training generative models based on unrolling a variational auto-encoder into a Markov chain, and shaping the chain's trajectories using a technique inspired by recent work in Approximate Bayesian computation. We show that the global minimizer of the resulting objective is achieved when the generative model reproduces the target distribution. To allow finer control over the behavior of the models, we add a regularization term inspired by techniques used for regularizing certain types of policy search in reinforcement learning. We present empirical results on the MNIST and TFD datasets which show that our approach offers state-of-the-art performance, both quantitatively and from a qualitative point of view.
Date of publication 2017
Code Programming Language Python
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