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Prescribed Generative Adversarial Network

Introduced by Dieng et al. in Prescribed Generative Adversarial Networks

Prescribed GANs add noise to the output of a density network and optimize an entropy-regularized adversarial loss. The added noise renders tractable approximations of the predictive log-likelihood and stabilizes the training procedure. The entropy regularizer encourages PresGANs to capture all the modes of the data distribution. Fitting PresGANs involves computing the intractable gradients of the entropy regularization term; PresGANs sidestep this intractability using unbiased stochastic estimates.

Source: Prescribed Generative Adversarial Networks

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PAPER DATE
Prescribed Generative Adversarial Networks
| Adji B. DiengFrancisco J. R. RuizDavid M. BleiMichalis K. Titsias
2019-10-09

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