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Density Estimation Using Real NVP
This dataset has no description
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Alternating Back-Propagation for Generator Networks
This dataset has no description
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Wasserstein GAN
This dataset has no description
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Flexible Prior Distributions for Deep Generative Models
The dataset induced prior distribution is learned using a secondary GAN named PGAN. This prior is then used to further train the original GAN. -
Max-Margin Deep Generative Models
Deep generative models (DGMs) are effective on learning multilayered represen- tations of complex data and performing inference of input data by exploring the generative ability. -
Adversarial Feature Learning
Adversarial Feature Learning -
Generative Adversarial Networks
Generative Adversarial Networks (GANs) consist of two networks: a generator G(z) and a discriminator D(x). The discriminator is trying to distinguish real objects from objects...
