ProjectDFG SPP 2298-Hein – Towards everywhere reliable classification - A joint framework for adversarial robustness and…
Basic data
Acronym:
DFG SPP 2298-Hein
Title:
Towards everywhere reliable classification - A joint framework for adversarial robustness and out-of-distribution detection
Duration:
16/06/2021 to 15/06/2024
Abstract / short description:
Adversarial robustness and out-of-distribution (OOD) detection have been treated
separately so far. However, the separation of these problems is
artificial as they are inherently linked to each other. Advances in adversarial robustness
generalizing beyond the threat models used at training time seem possible only
by going beyond the classical adversarial training framework proposed and merging
OOD detection and adversarial robustness in a single framework.
separately so far. However, the separation of these problems is
artificial as they are inherently linked to each other. Advances in adversarial robustness
generalizing beyond the threat models used at training time seem possible only
by going beyond the classical adversarial training framework proposed and merging
OOD detection and adversarial robustness in a single framework.
Involved staff
Managers
Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science
Department of Informatics, Faculty of Science
Cluster of Excellence: Machine Learning: New Perspectives for Science (CML)
Centers or interfaculty scientific institutions
Centers or interfaculty scientific institutions
Tübingen AI Center
Department of Informatics, Faculty of Science
Department of Informatics, Faculty of Science
Local organizational units
Department of Informatics
Faculty of Science
University of Tübingen
University of Tübingen
Funders
Bonn, Nordrhein-Westfalen, Germany