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Learned approximate inference can be performed by training an auxiliary network to predict z given x. This is similar to the inference net trained by the wake-sleep algorithm but with the advantage that the inference net may be trained for a fixed generator net after the generato...

Generative Adversarial Nets.

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014), (2014): 2672-2680

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Abstract

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is ...More

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  • Proposes a new framework for estimating generative models via an adversarial process, in which trains two models: a generative model G
  • Proposes a new generative model estimation procedure that sidesteps these difficulties
  • Explores the special case when the generative model generates samples by passing random noise through a multilayer perceptron, and the discriminative model is a multilayer perceptron
  • Were unaware at the time developed this work that Kingma and Welling and Rezende et al. had developed more general stochastic backpropagation rules, allowing one to backpropagate through Gaussian distributions with finite variance, and to backpropagate to the covariance parameter as well as the mean
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