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arXiv · 1706.05477

Bayesian Conditional Generative Adverserial Networks

M. Ehsan Abbasnejad·Qinfeng Shi·Iman Abbasnejad·Anton van den Hengel·Anthony Dick
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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.

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M. Ehsan Abbasnejad, Qinfeng Shi, Iman Abbasnejad, Anton van den Hengel, Anthony Dick. 2017-06-17. Bayesian Conditional Generative Adverserial Networks. https://arxiv.org/abs/1706.05477

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