176 citations · 211 across the 16 of their papers we have counts for
24 papers
Does Medical Imaging learn different Convolution Filters?
Paul Gavrikov, Janis Keuper
Recent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on averag…
Robust Models are less Over-Confident
Julia Grabinski, Paul Gavrikov, Janis Keuper +1
Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental c…
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
Paul Gavrikov, Janis Keuper
We present first empirical results from our ongoing investigation of distribution shifts in image data used for various computer vision tasks. Instead of analyzing the original tra…
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces
Ricard Durall, Jireh Jam, Dominik Strassel +2
Facial image manipulation is a generation task where the output face is shifted towards an intended target direction in terms of facial attribute and styles. Recent works have achi…
Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space
Kalun Ho, Franz-Josef Pfreundt, Janis Keuper +1
Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on sta…
Combining Transformer Generators with Convolutional Discriminators
Ricard Durall, Stanislav Frolov, Jörn Hees +4
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed wit…