3 papers
cs.LG2020
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…
cs.LG2019
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.…
cs.LG2019
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…