1 code implementation • 31 Jan 2022 • Philipp Oberdiek, Gernot A. Fink, Matthias Rottmann
We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs).
Out of Distribution (OOD) Detection Uncertainty Quantification
1 code implementation • 14 May 2020 • Philipp Oberdiek, Matthias Rottmann, Gernot A. Fink
When deploying deep learning technology in self-driving cars, deep neural networks are constantly exposed to domain shifts.
no code implementations • 26 Mar 2019 • Fabian Wolf, Philipp Oberdiek, Gernot A. Fink
In recent years, convolutional neural networks (CNNs) took over the field of document analysis and they became the predominant model for word spotting.
no code implementations • 22 May 2018 • Philipp Oberdiek, Matthias Rottmann, Hanno Gottschalk
If we however allow the meta classifier to be trained on uncertainty metrics for some out-of-distribution samples, meta classification for concepts remote from EMNIST digits (then termed known unknowns) can be improved considerably.