3 citations · 3 across the 3 of their papers we have counts for
5 papers · 1 filter
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing
Kanil Patel, William Beluch, Kilian Rambach +2
Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the classification…
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra
Kanil Patel, William Beluch, Kilian Rambach +3
Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar.In addition to high accuracy, it is crucial for decision ma…
Bosch Deep Learning Hardware Benchmark
Armin Runge, Thomas Wenzel, Dimitrios Bariamis +3
The widespread use of Deep Learning (DL) applications in science and industry has created a large demand for efficient inference systems. This has resulted in a rapid increase of a…
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration
Kanil Patel, William Beluch, Dan Zhang +2
Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks.…
Robust Anomaly Detection in Images using Adversarial Autoencoders
Laura Beggel, Michael Pfeiffer, Bernd Bischl
Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neur…