Variational Generative Stochastic Networks with Collaborative Shaping

By Philip Bachman, Doina Precup. 2015

13 citations recorded by the source corpus.

We develop an approach to training generative models based on unrolling a variational autoencoder 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability, Generative Models

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Citations in the corpus

Are GANs Created Equal? A Large-Scale Study (2017, background): “In addition, ranking models based on these estimates is discouraged [4].”
A note on the evaluation of generative models (2015, neutral): “Counter to this idea, Parzen window estimates of the likelihood have been observed to produce rankings different from other estimates (Bachman & Precup, 2015).”