43 citations · 67 across the 4 of their papers we have counts for
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cs.CV2018★ 12 cited
Disentangling Multiple Conditional Inputs in GANs
Gökhan Yildirim, Calvin Seward, Urs Bergmann
In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in…
cs.LG2018★ 5 cited
First Order Generative Adversarial Networks
Calvin Seward, Thomas Unterthiner, Urs Bergmann +2
GANs excel at learning high dimensional distributions, but they can update generator parameters in directions that do not correspond to the steepest descent direction of the object…