3 papers
cs.CV2020
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation
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. These include, e.g., changes in weather conditions, time…
cs.CV2019
Exploring Confidence Measures for Word Spotting in Heterogeneous Datasets
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. Especially attribute CNNs, w…
cs.LG2018
Classification Uncertainty of Deep Neural Networks Based on Gradient Information
Philipp Oberdiek, Matthias Rottmann, Hanno Gottschalk
We study the quantification of uncertainty of Convolutional Neural Networks (CNNs) based on gradient metrics. Unlike the classical softmax entropy, such metrics gather information…