4 papers
Algebraic Multigrid Acceleration for Efficient Label Spreading
Antonia van Betteray, Jonathan Klees, Miriam Schäfers +1
Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming. Label spreading is a semi-…
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…
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…
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions
Antonia van Betteray, Matthias Rottmann, Karsten Kahl
Current state-of-the-art deep neural networks for image classification are made up of 10 - 100 million learnable weights and are therefore inherently prone to overfitting. The comp…