Urs Bergmann
Cited by
Cited by
Texture synthesis with spatial generative adversarial networks
N Jetchev, U Bergmann, R Vollgraf
NIPS 2016 adversarial learning workshop, Barcelona, Spain, 2016
The conditional analogy gan: Swapping fashion articles on people images
N Jetchev, U Bergmann
International Conference on Computer Vision 2017 - Computer Vision for …, 2017
Learning texture manifolds with the periodic spatial GAN
U Bergmann, N Jetchev, R Vollgraf
International Conference for Machine Learning (ICML) 2017, 2017
Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
K Rasul, AS Sheikh, I Schuster, U Bergmann, R Vollgraf
International Conference on Learning Representations (ICLR 2021), 2021
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
G Yildirim, N Jetchev, R Vollgraf, U Bergmann
International Conference on Computer Vision, ICCV 2019, Workshop on Computer …, 2019
A hierarchical bayesian model for size recommendation in fashion
R Guigourès, YK Ho, E Koriagin, AS Sheikh, U Bergmann, R Shirvany
Proceedings of the 12th ACM conference on recommender systems, 392-396, 2018
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
AS Sheikh, R Guigoures, E Koriagin, YK Ho, R Shirvany, R Vollgraf, ...
Thirteenth ACM Conference on Recommender Systems (RecSys '19), September 16 …, 2019
Disentangling Multiple Conditional Inputs in GANs
G Yildirim, C Seward, U Bergmann
In Proceedings of KDD Fashion Workshop (KDD), 2018
Self-organization of topographic bilinear networks for invariant recognition
U Bergmann, C Von Der Malsburg
Neural Computation 23 (11), 2770-2797, 2011
Meta-Learning for Size and Fit Recommendation in Fashion
J Lasserre, AS Sheikh, E Koriagin, U Bergmann, R Vollgraf, R Shirvany
Proceedings of the 2020 SIAM International Conference on Data Mining, 55-63, 2020
Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations
MSM Sajjadi, H Meyer, E Pot, U Bergmann, K Greff, N Radwan, S Vora, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Stochastic Maximum Likelihood Optimization via Hypernetworks
AS Sheikh, K Rasul, A Merentitis, U Bergmann
31st Conference on Neural Information Processing Systems (NIPS 2017), Long …, 2017
GANosaic: Mosaic Creation with Generative Texture Manifolds
N Jetchev, U Bergmann, C Seward
31st Conference on Neural Information Processing Systems (NIPS 2017), Long …, 2017
Self-organization of steerable topographic mappings as basis for translation invariance
J Zhu, U Bergmann, C Malsburg
International Conference on Artificial Neural Networks, 414-419, 2010
First Order Generative Adversarial Networks
C Seward, T Unterthiner, U Bergmann, N Jetchev, S Hochreiter
35th International Conference on Machine Learning (ICML), 2018
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages
N Jetchev, U Bergmann, G Yildirim
NeurIPS 2019 Workshop on Machine Learning for Creativity and Design, 2019
Set flow: A permutation invariant normalizing flow
K Rasul, I Schuster, R Vollgraf, U Bergmann
arXiv preprint arXiv:1909.02775, 2019
Ontogenesis of invariance transformations
U Bergmann, C von der Malsburg
Computational and Systems Neuroscience (Cosyne), 2008
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
A Merentitis, K Rasul, R Vollgraf, AS Sheikh, U Bergmann
32nd Conference on Neural Information Processing Systems (NIPS 2018 …, 2018
Copy the Old or Paint Anew
N Jetchev, U Bergmann, G Yildirim
An Adversarial Framework for (non-) Parametric Image Stylization. CoRR abs …, 2018
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