Improving and Evaluating the Corruption Robustness of Image Classifiers using random p-norm Noise

The complete article "Improving and Evaluating the Corruption Robustness of Image Classifiers using random p-norm Noise" is a chapter of the book "Computer Vision, Imaging and Computer Graphics Theory and Applications. 19th International Joint Conference, VISIGRAPP 2024, Rome, Italy, February 27-29, 2024, Revised Selected Papers" (charges may apply).

Bibliographic information

Title:  Improving and Evaluating the Corruption Robustness of Image Classifiers using random p-norm Noise. 

Written by:  G. Siedel, W. Shao, S. Vock, A. Morozov

in: Computer Vision, Imaging and Computer Graphics Theory and Applications. 19th International Joint Conference, VISIGRAPP 2024, Rome, Italy, February 27-29, 2024, Revised Selected Papers Cham:  Springer, 2025.  pages: 1-20, Project number: F 2497, DOI: 10.1007/978-3-031-93418-6_23

Further Information

Research Project

Project numberF 2497 StatusCompleted Project Safety-related risk assessment of a cyber-physical model system for industry 4.0 applications

To the Project

Research completed