Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

6 Nov 2016 Dilin Wang Qiang Liu

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient that maximumly decreases the KL divergence with the target distribution... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Conditional Image Generation CIFAR-10 SteinGAN Inception score 6.35 # 13

Methods used in the Paper


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