176 citations · 336 across the 51 of their papers we have counts for
9 papers · 1 filter
Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
Peter Lorenz, Margret Keuper, Janis Keuper
Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial per…
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…
Deep Diffusion Models for Seismic Processing
Ricard Durall, Ammar Ghanim, Mario Fernandez +2
Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such a…
Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal
Ricard Durall, Ammar Ghanim, Norman Ettrich +1
Seismic processing often requires suppressing multiples that appear when collecting data. To tackle these artifacts, practitioners usually rely on Radon transform-based algorithms…
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters
Paul Gavrikov, Janis Keuper
Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongo…
Adversarial Robustness through the Lens of Convolutional Filters
Paul Gavrikov, Janis Keuper
Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models…
FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting
Julia Grabinski, Steffen Jung, Janis Keuper +1
Over the last years, Convolutional Neural Networks (CNNs) have been the dominating neural architecture in a wide range of computer vision tasks. From an image and signal processing…
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…