3 papers
cs.LG2025
LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis
Antonia van Betteray, Matthias Rottmann, Karsten Kahl
The singular values of convolutional mappings encode interesting spectral properties, which can be used, e.g., to improve generalization and robustness of convolutional neural netw…
cs.LG2025
Poly-MgNet: Polynomial Building Blocks in Multigrid-Inspired ResNets
Antonia van Betteray, Matthias Rottmann, Karsten Kahl
The structural analogies of ResNets and Multigrid (MG) methods such as common building blocks like convolutions and poolings where already pointed out by He et al.\ in 2016. Multig…
cs.CV2024
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion
Edgar Heinert, Matthias Rottmann, Kira Maag +1
Convolutional neural networks (CNNs) for image processing tend to focus on localized texture patterns, commonly referred to as texture bias. While most of the previous works in the…