Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

26 Feb 2017Tong CheYanran LiRuixiang ZhangR Devon HjelmWenjie LiYangqiu SongYoshua Bengio

Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted. The fundamental reason is the difficulty of back-propagation through discrete random variables combined with the inherent instability of the GAN training objective... (read more)

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