Adapting imagenet-scale models to complex distribution shifts with self-learning
Abstract
Adapting ImageNet-scale models to complex distribution shifts with self-learning Adapting ImageNet-scale models to complex distribution shifts with self-learning DSpace Repositorium (Manakin basiert) Einloggen Publikationsdienste → Universitätsbibliographie → 7 Mathematisch-Naturwissenschaftliche Fakultät → Dokumentanzeige « zurück Adapting ImageNet-scale models to complex distribution shifts with self-learning Autor(en): Rusak, Evgenia; Schneider, Steffen; Gehler, Peter; Bringmann, Oliver; Brendel, Wieland; Bethge, Matthias Tübinger Autor(en): Rusak, Evgenia Schneider, Steffen Bringmann, Oliver Brendel, Wieland Bethge, Matthias Erschienen in: ArXiv (2021-04-28), Bd. 2104.12928 Verlagsangabe: Cornell University Sprache: Englisch Referenz zum Volltext: https://arxiv.org/abs/2104.12928 DDC-Klassifikation: 004 - Informatik Dokumentart: Preprint Zur Langanzeige Das Dokument erscheint in: 7 …
Wieland Brendel
Principal Investigator (PI)
Wieland Brendel received his Diploma in physics from the University of Regensburg (2010) and his Ph.D. in computational neuroscience from the École normale supérieure in Paris (2014). He joined the University of Tübingen as a postdoctoral researcher in the group of Matthias Bethge, became a Principal Investigator and Team Lead in the Tübingen AI Center (2018) and an Emmy Noether Group Leader for Robust Machine Learning (2020). In May 2022, Wieland joined the Max-Planck Institute for Intelligent Systems as an independent Group Leader and is now a Hector-endowed Fellow at the ELLIS Institute Tübingen (since September 2023). He received the 2023 German Pattern Recognition Award for his substantial contributions on robust, generalisable and interpretable machine vision. Aside of his research, Wieland co-founded a nationwide school competition (bw-ki.de) and a machine learning startup focused on visual quality control.