Dr. rer. nat. Christian Reimers
Curriculum Vitae
2017-2021 Research Associate in the Comupter Vision Group Friedrich-Schiller-Universität Jena and Climate Informatics Group, DLR Institute
2015/2016 Yearlong internship at Nonlinear Dynamics Group Max Planck Institute for Dynamics and Self-Organization in Göttingen
until 2017 M. Sc. studies in Mathematics at Göttingen University
until 2013 B. Sc. studies in Mathematics at Göttingen University
Projects
Research Interests
  • Deep Learning
Publications
2022
Niklas Penzel, Christian Reimers, Paul Bodesheim, Joachim Denzler:
Investigating Neural Network Training on a Feature Level using Conditional Independence.
ECCV Workshop on Causality in Vision (ECCV-WS). Pages 383-399. 2022.
[bibtex] [pdf] [doi] [abstract]
Xavier-Andoni Tibau, Christian Reimers, Andreas Gerhardus, Joachim Denzler, Veronika Eyring, Jakob Runge:
A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections.
Environmental Data Science. 1 : pp. E12. 2022.
[bibtex] [web] [doi] [abstract]
2021
Christian Reimers, Niklas Penzel, Paul Bodesheim, Jakob Runge, Joachim Denzler:
Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification.
CVPR ISIC Skin Image Analysis Workshop (CVPR-WS). Pages 1810-1819. 2021.
[bibtex] [pdf] [web] [abstract]
Christian Reimers, Paul Bodesheim, Jakob Runge, Joachim Denzler:
Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 48-62. 2021.
[bibtex] [pdf] [doi] [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]
2020
Christian Reimers, Christian Requena-Mesa:
Deep Learning--an Opportunity and a Challenge for Geo-and Astrophysics.
2020.
[bibtex]
Christian Reimers, Jakob Runge, Joachim Denzler:
Determining the Relevance of Features for Deep Neural Networks.
European Conference on Computer Vision. Pages 330-346. 2020.
[bibtex] [abstract]
2019
Christian Reimers, Jakob Runge, Joachim Denzler:
Using Causal Inference to Globally Understand Black Box Predictors Beyond Saliency Maps.
International Workshop on Climate Informatics (CI). 2019.
[bibtex] [pdf] [doi] [abstract]
Xavier-Andoni Tibau, Christian Reimers, Veronika Eyring, Joachim Denzler, Markus Reichstein, Jakob Runge:
Toy models to analyze emergent constraint approaches.
European Geosciences Union General Assembly (EGU): Abstract + Poster Presentation. 2019.
[bibtex] [pdf] [web] [abstract]
2018
Xavier-Andoni Tibau, Christian Requena-Mesa, Christian Reimers, Joachim Denzler, Veronika Eyring, Markus Reichstein, Jakob Runge:
SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data.
American Geophysical Union Fall Meeting (AGU): Abstract + Poster Presentation. 2018.
[bibtex] [web] [abstract]
Xavier-Andoni Tibau, Christian Requena-Mesa, Christian Reimers, Joachim Denzler, Veronika Eyring, Markus Reichstein, Jakob Runge:
SupernoVAE: VAE based Kernel-PCA for Analysis of Spatio-Temporal Earth Data.
International Workshop on Climate Informatics (CI). Pages 73-76. 2018.
[bibtex] [web] [doi] [abstract]