no code implementations • 8 Jan 2024 • Jonas Dippel, Barbara Feulner, Tobias Winterhoff, Simon Schallenberg, Gabriel Dernbach, Andreas Kunft, Stephan Tietz, Timo Milbich, Simon Heinke, Marie-Lisa Eich, Julika Ribbat-Idel, Rosemarie Krupar, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen, Maximilian Alber
Histopathology plays a central role in clinical medicine and biomedical research.
no code implementations • 3 Feb 2023 • Miriam Hägele, Johannes Eschrich, Lukas Ruff, Maximilian Alber, Simon Schallenberg, Adrien Guillot, Christoph Roderburg, Frank Tacke, Frederick Klauschen
Motivated by the medical application, we demonstrate for general segmentation tasks that including additional patches with solely weak complementary labels during model training can significantly improve the predictive performance and robustness of a model.
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)
no code implementations • 29 Sep 2021 • Jannik Wolff, Rahul G Krishnan, Lukas Ruff, Jan Nikolas Morshuis, Tassilo Klein, Shinichi Nakajima, Moin Nabi
Humans find structure in natural phenomena by absorbing stimuli from multiple input sources such as vision, text, and speech.
no code implementations • 5 Oct 2020 • Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen
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.
no code implementations • 29 Sep 2020 • Michael Joswig, Marek Kaluba, Lukas Ruff
We propose a new geometric method for measuring the quality of representations obtained from deep learning.
no code implementations • 24 Sep 2020 • Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller
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.
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)
no code implementations • 18 Jun 2020 • Jacob Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller
The 'Clever Hans' effect occurs when the learned model produces correct predictions based on the 'wrong' features.
Anomaly Detection Explainable Artificial Intelligence (XAI) +1
1 code implementation • 30 May 2020 • Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous."
no code implementations • 24 Jan 2020 • Penny Chong, Lukas Ruff, Marius Kloft, Alexander Binder
However, deep SVDD suffers from hypersphere collapse -- also known as mode collapse, if the architecture of the model does not comply with certain architectural constraints, e. g. the removal of bias terms.
1 code implementation • ACL 2019 • Lukas Ruff, Yury Zemlyanskiy, V, Robert ermeulen, Thomas Schnake, Marius Kloft
There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.
no code implementations • 18 Jun 2019 • Jacob Kauffmann, Malte Esders, Lukas Ruff, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller
Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features.
7 code implementations • ICLR 2020 • Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
1 code implementation • ICML 2018 • Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Ranked #32 on Anomaly Detection on One-class CIFAR-10
no code implementations • ICLR 2018 • Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.