176 citations · 255 across the 11 of their papers we have counts for
4 papers · 1 filter
Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues
Ricard Durall, Avraam Chatzimichailidis, Peter Labus +1
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This i…
Latent Space Conditioning on Generative Adversarial Networks
Ricard Durall, Kalun Ho, Franz-Josef Pfreundt +1
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this tr…
Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions
Ricard Durall, Margret Keuper, Janis Keuper
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or vid…
Local Facial Attribute Transfer through Inpainting
Ricard Durall, Franz-Josef Pfreundt, Janis Keuper
The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction,…