42 citations · 80 across the 28 of their papers we have counts for
3 papers · 1 filter
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
Felix Meissen, Johannes Getzner, Alexander Ziller +4
Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the…
SmoothNets: Optimizing CNN architecture design for differentially private deep learning
Nicolas W. Remerscheid, Alexander Ziller, Daniel Rueckert +1
The arguably most widely employed algorithm to train deep neural networks with Differential Privacy is DPSGD, which requires clipping and noising of per-sample gradients. This intr…
Oktoberfest Food Dataset
Alexander Ziller, Julius Hansjakob, Vitalii Rusinov +3
We release a realistic, diverse, and challenging dataset for object detection on images. The data was recorded at a beer tent in Germany and consists of 15 different categories of…