Original paper
Stabilizing Training of Generative Adversarial Networks through Regularization
Abstract
Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters. This fragility is in part due to a dimensional mismatch or non-overlapping support between the model distribution and the data distribution, causing their density ratio and the associated f -divergence to...
Paper Details
Title
Stabilizing Training of Generative Adversarial Networks through Regularization
Published Date
May 25, 2017
Volume
30
Pages
2018 - 2028
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