27 citations · 42 across the 12 of their papers we have counts for
12 papers
How Do Training Methods Influence the Utilization of Vision Models?
Paul Gavrikov, Shashank Agnihotri, Margret Keuper +1
Not all learnable parameters (e.g., weights) contribute equally to a neural network's decision function. In fact, entire layers' parameters can sometimes be reset to random values…
Can Biases in ImageNet Models Explain Generalization?
Paul Gavrikov, Janis Keuper
The robust generalization of models to rare, in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is…
Can We Talk Models Into Seeing the World Differently?
Paul Gavrikov, Jovita Lukasik, Steffen Jung +4
Unlike traditional vision-only models, vision language models (VLMs) offer an intuitive way to access visual content through language prompting by combining a large language model…
Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers
Paul Gavrikov, Janis Keuper
Assessing the robustness of deep neural networks against out-of-distribution inputs is crucial, especially in safety-critical domains like autonomous driving, but also in safety sy…
On the Interplay of Convolutional Padding and Adversarial Robustness
Paul Gavrikov, Janis Keuper
It is common practice to apply padding prior to convolution operations to preserve the resolution of feature-maps in Convolutional Neural Networks (CNN). While many alternatives ex…
An Extended Study of Human-like Behavior under Adversarial Training
Paul Gavrikov, Janis Keuper, Margret Keuper
Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by…
The Power of Linear Combinations: Learning with Random Convolutions
Paul Gavrikov, Janis Keuper
Following the traditional paradigm of convolutional neural networks (CNNs), modern CNNs manage to keep pace with more recent, for example transformer-based, models by not only incr…
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…