A02 Scattering transforms of sparse signals
The scattering transform is based on a designed convolutional neural network using a wavelet filter bank structure. Other filter banks have been proposed as basis of the scattering transform, but the impact of this choice is currently unclear. This project investigates the relevance of signal sparsity for the analysis of the scattering transform, with the aim of assessing the potential of other filter banks in this context. The fundamental hypothesis that we intend to investigate is that unstructured sparsity is generally not well-suited to describe relevant features of the response of scattering transforms, but that structured sparsity is.
Project Leader
- Prof. Dr. Hartmut Führ
- RWTH Aachen University
- more information
- +49 241 80 94527
- fuehr@mathga.rwth-aachen.de
- homepage
Doctoral Researcher
- Max Getter
- RWTH Aachen University
- more information
- +49 241 80 90628
- getter@mathga.rwth-aachen.de
Publications
Energy Propagation in Scattering Convolution Networks Can Be Arbitrarily Slow
Preprint 2024