18 citations · 20 across the 2 of their papers we have counts for
6 papers · 1 filter
Deep Anomaly Detection by Residual Adaptation
Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen +1
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality. In th…
Geometric Disentanglement by Random Convex Polytopes
Michael Joswig, Marek Kaluba, Lukas Ruff
We propose a new geometric method for measuring the quality of representations obtained from deep learning. Our approach, called Random Polytope Descriptor, provides an efficient d…
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen +5
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.…
The Clever Hans Effect in Anomaly Detection
Jacob Kauffmann, Lukas Ruff, Grégoire Montavon +1
The 'Clever Hans' effect occurs when the learned model produces correct predictions based on the 'wrong' features. This effect which undermines the generalization capability of an…
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification
Penny Chong, Lukas Ruff, Marius Kloft +1
Anomaly detection algorithms find extensive use in various fields. This area of research has recently made great advances thanks to deep learning. A recent method, the deep Support…
Deep Semi-Supervised Anomaly Detection
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz +4
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsuperv…