no code implementations • 7 May 2024 • Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type.
no code implementations • 22 Feb 2024 • Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Sophie Fellenz, Marius Kloft
Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous.
no code implementations • 10 Mar 2023 • Fabian Hartung, Billy Joe Franks, Tobias Michels, Dennis Wagner, Philipp Liznerski, Steffen Reithermann, Sophie Fellenz, Fabian Jirasek, Maja Rudolph, Daniel Neider, Heike Leitte, Chen Song, Benjamin Kloepper, Stephan Mandt, Michael Bortz, Jakob Burger, Hans Hasse, Marius Kloft
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
1 code implementation • 23 May 2022 • Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft
We find that standard classifiers and semi-supervised one-class methods trained to discern between normal samples and relatively few random natural images are able to outperform the current state of the art on an established AD benchmark with ImageNet.
Ranked #1 on Anomaly Detection on One-class CIFAR-10 (using extra training data)
2 code implementations • ICLR 2021 • Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.
Ranked #5 on Anomaly Detection on One-class ImageNet-30 (using extra training data)