The voraus-AD Dataset for Anomaly Detection in Robot Applications
Author:
Affiliation:
1. voraus robotik GmbH, Hanover, Germany
2. Institute for Information Processing, L3S, Leibniz University Hannover, Hannover, Germany
3. Computer Vision Laboratory, Linköping University, Linköping, Sweden
Funder
Bundesministerium für Bildung und Forschung
AI service center KISSKI
Deutsche Forschungsgemeinschaft
Germany's Excellence Strategy
Cluster of Excellence PhoenixD
German Federal Ministry of the Environment
Nature Conservation, Nuclear Safety and Consumer Protection
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/8860/10365208/10315239.pdf?arnumber=10315239
Reference55 articles.
1. Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer;Amer,2012
2. Guided image generation with conditional invertible neural networks;Ardizzone,2019
3. A Minimally Supervised Approach Based on Variational Autoencoders for Anomaly Detection in Autonomous Robots
4. HMMs for anomaly detection in autonomous robots;Azzalini,2020
5. INDUSTRY 4.0: A REVIEW ON INDUSTRIAL AUTOMATION AND ROBOTIC
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