107 citations · 167 across the 9 of their papers we have counts for
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cs.CV2018★ 4 cited
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Nikolay Jetchev, Urs Bergmann, Gokhan Yildirim
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-int…
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…