107 citations · 324 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Stochastic Maximum Likelihood Optimization via Hypernetworks
Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis +1
This work explores maximum likelihood optimization of neural networks through hypernetworks. A hypernetwork initializes the weights of another network, which in turn can be employe…