27 citations · 42 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…