9 citations · 21 across the 6 of their papers we have counts for
3 papers · 1 filter
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
Jan-Philipp Schulze, Philip Sperl, Ana Răduţoiu +2
Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss. Samples unlike the ones seen during training cause a…
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators
J. -P. Schulze, P. Sperl, K. Böttinger
Anomaly detection is a challenging task for machine learning algorithms due to the inherent class imbalance. It is costly and time-demanding to manually analyse the observed data,…
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…