Search Results for author: Katharina Rombach

Found 5 papers, 0 papers with code

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

no code implementations28 Aug 2022 Katharina Rombach, Gabriel Michau, Wilfried Bürzle, Stefan Koller, Olga Fink

Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods.

Anomaly Detection Contrastive Learning +2

Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation

no code implementations29 Apr 2022 Katharina Rombach, Dr. Gabriel Michau, Prof. Dr. Olga Fink

To enable a better transferability of the trained models between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN.

Partial Domain Adaptation

Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data

no code implementations30 Sep 2020 Katharina Rombach, Gabriel Michau, Olga Fink

In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space.

Data Augmentation Fault Detection

Automated Machine Learning in Practice: State of the Art and Recent Results

no code implementations19 Jul 2019 Lukas Tuggener, Mohammadreza Amirian, Katharina Rombach, Stefan Lörwald, Anastasia Varlet, Christian Westermann, Thilo Stadelmann

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions.

AutoML BIG-bench Machine Learning +1

Deep Learning in the Wild

no code implementations13 Jul 2018 Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.

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