29 citations · 77 across the 22 of their papers we have counts for
3 papers · 1 filter
Optimizing Information Loss Towards Robust Neural Networks
Philip Sperl, Konstantin Böttinger
Neural Networks (NNs) are vulnerable to adversarial examples. Such inputs differ only slightly from their benign counterparts yet provoke misclassifications of the attacked NNs. Th…
Anomaly Detection by Recombining Gated Unsupervised Experts
J. -P. Schulze, P. Sperl, K. Böttinger
Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some…
: Activation Anomaly Analysis
Philip Sperl, Jan-Philipp Schulze, Konstantin Böttinger
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain informati…