Novelty Detection

Team

Violeta Teodora Trifunov, Maha Shadaydeh, Paul Bodesheim

Motivation

In many important learning tasks, training examples are often available for only one class or a limited set of classes. Learning in this scenario is difficult, since training examples from “outside” are not available at all. A general term often used is out-of-distribution detection. For binary classification tasks, this problem is also known as one-class classification (OCC), outlier detection, or anomaly detection. In a multi-class setup, it is referred to as novelty detection,  novel object class detection, object discovery, open-set recognition, or open-world recognition. Our work in this area addresses the use of methods for deriving a so-called novelty score or anomaly score to identify samples that do not belong to the distribution of known concepts derived from given training data.

Publications

2024
Kim Bjerge, Paul Bodesheim, Henrik Karstoft:
Few-Shot Learning with Novelty Detection.
International Conference on Deep Learning Theory and Applications (DeLTA). 2024. Best Paper Award
[bibtex] [web] [abstract]
2021
Martin Thümmel, Sven Sickert, Joachim Denzler:
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection.
IEEE International Workshop on Biometrics and Forensics (IWBF). Pages 1-6. 2021.
[bibtex] [web] [doi] [code] [abstract]
Niklas Penzel, Christian Reimers, Clemens-Alexander Brust, Joachim Denzler:
Investigating the Consistency of Uncertainty Sampling in Deep Active Learning.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 159-173. 2021.
[bibtex] [pdf] [web] [doi] [abstract]
Violeta Teodora Trifunov, Maha Shadaydeh, Björn Barz, Joachim Denzler:
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning.
IEEE International Conference on Machine Learning and Applications (ICMLA). Pages 166-172. 2021.
[bibtex] [pdf] [web] [doi] [abstract]
2019
Björn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler:
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 41 (5) : pp. 1088-1101. 2019. (Pre-print published in 2018.)
[bibtex] [pdf] [web] [doi] [code] [abstract]
Violeta Teodora Trifunov, Maha Shadaydeh, Jakob Runge, Veronika Eyring, Markus Reichstein, Joachim Denzler:
Nonlinear Causal Link Estimation under Hidden Confounding with an Application to Time-Series Anomaly Detection.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 261-273. 2019.
[bibtex] [pdf] [doi] [abstract]
Yanira Guanche, Maha Shadaydeh, Miguel Mahecha, Joachim Denzler:
Attribution of Multivariate Extreme Events.
International Workshop on Climate Informatics (CI). 2019.
[bibtex] [pdf] [abstract]
2018
Maha Shadaydeh, Yanira Guanche Garcia, Miguel Mahecha, Markus Reichstein, Joachim Denzler:
Causality analysis of ecological time series: a time-frequency approach.
International Workshop on Climate Informatics (CI). 2018.
[bibtex] [pdf]
Yanira Guanche Garcia, Maha Shadaydeh, Miguel Mahecha, Joachim Denzler:
Extreme anomaly event detection in biosphere using linear regression and a spatiotemporal MRF model.
Natural Hazards. pp. 1-19. 2018.
[bibtex] [pdf] [web] [doi] [abstract]
2017
Björn Barz, Yanira Guanche, Erik Rodner, Joachim Denzler:
Maximally Divergent Intervals for Extreme Weather Event Detection.
MTS/IEEE OCEANS Conference Aberdeen. Pages 1-9. 2017.
[bibtex] [pdf] [doi] [abstract]
Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebasitan Sippel, Miguel D. Mahecha:
Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques.
Earth System Dynamics. 8 (3) : pp. 677-696. 2017.
[bibtex] [pdf] [web] [doi] [abstract]
Yanira Guanche, Maha Shadaydeh, Miguel Mahecha, Joachim Denzler:
Biosphere Anomalies Detection by Regression Models.
Conference on Advances in Extreme Value Analysis and Application to Natural Hazards (EVAN). 2017.
[bibtex] [pdf]
2016
Alexander Freytag:
Lifelong Learning for Visual Recognition Systems.
2016. ISBN 9783843929950
[bibtex] [pdf] [web]
Christoph Käding, Erik Rodner, Alexander Freytag, Joachim Denzler:
Watch, Ask, Learn, and Improve: A Lifelong Learning Cycle for Visual Recognition.
European Symposium on Artificial Neural Networks (ESANN). Pages 381-386. 2016.
[bibtex] [pdf] [code] [presentation] [abstract]
Erik Rodner, Björn Barz, Yanira Guanche, Milan Flach, Miguel Mahecha, Paul Bodesheim, Markus Reichstein, Joachim Denzler:
Maximally Divergent Intervals for Anomaly Detection.
Workshop on Anomaly Detection (ICML-WS). 2016. Best Paper Award
[bibtex] [pdf] [web] [code] [abstract]
Milan Flach, Miguel Mahecha, Fabian Gans, Erik Rodner, Paul Bodesheim, Yanira Guanche-Garcia, Alexander Brenning, Joachim Denzler, Markus Reichstein:
Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations.
European Geosciences Union General Assembly (EGU): Abstract + Oral Presentation. 2016.
[bibtex] [pdf] [web] [abstract]
Milan Flach, Sebastian Sippel, Paul Bodesheim, Alexander Brenning, Joachim Denzler, Fabian Gans, Yanira Guanche, Markus Reichstein, Erik Rodner, Miguel D. Mahecha:
Hot spots of multivariate extreme anomalies in Earth observations.
American Geophysical Union Fall Meeting (AGU): Abstract + Oral Presentation. 2016.
[bibtex] [web] [abstract]
Yanira Guanche Garcia, Erik Rodner, Milan Flach, Sebastian Sippel, Miguel Mahecha, Joachim Denzler:
Detecting Multivariate Biosphere Extremes.
International Workshop on Climate Informatics (CI). Pages 9-12. 2016.
[bibtex] [web] [doi] [abstract]
2015
Christoph Käding, Alexander Freytag, Erik Rodner, Paul Bodesheim, Joachim Denzler:
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 4343-4352. 2015.
[bibtex] [pdf] [web] [doi] [code] [presentation] [supplementary] [abstract]
Paul Bodesheim, Alexander Freytag, Erik Rodner, Joachim Denzler:
Local Novelty Detection in Multi-class Recognition Problems.
IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 813-820. 2015.
[bibtex] [pdf] [web] [doi] [supplementary] [abstract]
2013
Michael Kemmler, Erik Rodner, Esther-Sabrina Wacker, Joachim Denzler:
One-class Classification with Gaussian Processes.
Pattern Recognition. 46 (12) : pp. 3507-3518. 2013.
[bibtex] [pdf] [doi]
Michael Kemmler, Erik Rodner, Petra R\"osch, J\"urgen Popp, Joachim Denzler:
Automatic Identification of Novel Bacteria using Raman Spectroscopy and Gaussian Processes.
Analytica Chimica Acta. 794 : pp. 29-37. 2013.
[bibtex] [pdf] [web] [supplementary]
Paul Bodesheim, Alexander Freytag, Erik Rodner, Joachim Denzler:
An Efficient Approximation for Gaussian Process Regression.
2013. Technical Report TR-FSU-INF-CV-2013-01
[bibtex] [pdf]
Paul Bodesheim, Alexander Freytag, Erik Rodner, Joachim Denzler:
Approximations of Gaussian Process Uncertainties for Visual Recognition Problems.
Scandinavian Conference on Image Analysis (SCIA). Pages 182-194. 2013.
[bibtex] [pdf] [web] [doi] [abstract]
Paul Bodesheim, Alexander Freytag, Erik Rodner, Michael Kemmler, Joachim Denzler:
Kernel Null Space Methods for Novelty Detection.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pages 3374-3381. 2013.
[bibtex] [pdf] [web] [doi] [code] [presentation] [abstract]
2012
Erik Rodner:
Lernen mit wenigen Beispielen für die visuelle Objekterkennung.
Ausgezeichnete Informatikdissertationen 2011. 2012. in german
[bibtex] [pdf] [web]
Paul Bodesheim, Erik Rodner, Alexander Freytag, Joachim Denzler:
Divergence-Based One-Class Classification Using Gaussian Processes.
British Machine Vision Conference (BMVC). Pages 50.1-50.11. 2012.
[bibtex] [pdf] [web] [doi] [presentation] [abstract]
2011
Erik Rodner:
Learning from Few Examples for Visual Recognition Problems.
2011.
[bibtex] [pdf] [web]
Erik Rodner, Esther-Sabrina Wacker, Michael Kemmler, Joachim Denzler:
One-Class Classification for Anomaly Detection in Wire Ropes with Gaussian Processes in a Few Lines of Code.
Machine Vision Applications (MVA). Pages 219-222. 2011.
[bibtex] [pdf]
2010
Michael Kemmler, Erik Rodner, Joachim Denzler:
One-Class Classification with Gaussian Processes.
Asian Conference on Computer Vision (ACCV). Pages 489-500. 2010.
[bibtex] [pdf] [presentation]