3 citations · 9 across the 15 of their papers we have counts for
8 papers
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…
Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
Lars Nieradzik, Jördis Sieburg-Rockel, Stephanie Helmling +4
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera.…
Fine-Grained Product Classification on Leaflet Advertisements
Daniel Ladwig, Bianca Lamm, Janis Keuper
In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years f…
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…
Physics-Informed Learning of Aerosol Microphysics
Paula Harder, Duncan Watson-Parris, Philip Stier +3
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…