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Bayesian Conditional Generative Adverserial Networks

Authors :
Abbasnejad, M. Ehsan
Shi, Qinfeng
Abbasnejad, Iman
Hengel, Anton van den
Dick, Anthony
Publication Year :
2017
Publisher :
arXiv, 2017.

Abstract

Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input $y'$ to a sample $\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outperforms the state-of-the-arts.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....5a573f486c2dfecb437f5faefc79810c
Full Text :
https://doi.org/10.48550/arxiv.1706.05477