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Christoph Käding
Christoph Käding
freelance machine learning engineer
Bestätigte E-Mail-Adresse bei ckaeding.net - Startseite
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Zitiert von
Zitiert von
Jahr
Fine-tuning Deep Neural Networks in Continuous Learning Scenarios
C Käding, E Rodner, A Freytag, J Denzler
ACCV Workshop on Interpretation and Visualization of Deep Neural Nets (ACCV-WS), 2016
1402016
Active Learning for Deep Object Detection
CA Brust, C Käding, J Denzler
arXiv preprint arXiv:1809.09875, 2018
1312018
Towards Automated Visual Monitoring of Individual Gorillas in the Wild
CA Brust, T Burghardt, M Groenenberg, C Käding, HS Kühl, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
892017
Active learning and discovery of object categories in the presence of unnameable instances
C Käding, A Freytag, E Rodner, P Bodesheim, J Denzler
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on …, 2015
702015
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes
C Käding, E Rodner, A Freytag, J Denzler
NIPS Workshop on Continual Learning and Deep Networks (NIPS-WS), 2016
642016
Active Learning for Regression Tasks with Expected Model Output Changes
C Käding, E Rodner, A Freytag, O Mothes, B Barz, J Denzler
372018
Finding the unknown: Novelty detection with extreme value signatures of deep neural activations
A Schultheiss, C Käding, A Freytag, J Denzler
Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland …, 2017
252017
Active and incremental learning with weak supervision
CA Brust, C Käding, J Denzler
KI-Künstliche Intelligenz 34 (2), 165-180, 2020
232020
Large-scale active learning with approximations of expected model output changes
C Käding, A Freytag, E Rodner, A Perino, J Denzler
Pattern Recognition: 38th German Conference, GCPR 2016, Hannover, Germany …, 2016
222016
Watch, Ask, Learn, and Improve: A Lifelong Learning Cycle for Visual Recognition
C Käding, E Rodner, A Freytag, J Denzler
European Symposium on Artificial Neural Networks (ESANN), 2016
142016
Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research—individual identification and …
P Bodesheim, J Blunk, M Körschens, CA Brust, C Käding, J Denzler
Mammalian Biology 102 (3), 875-897, 2022
132022
Information-theoretic active learning for content-based image retrieval
B Barz, C Käding, J Denzler
German Conference on Pattern Recognition, 650-666, 2018
132018
Fast Learning and Prediction for Object Detection using Whitened CNN Features
B Barz, E Rodner, C Käding, J Denzler
arXiv preprint arXiv:1704.02930, 2017
42017
Universal eye-tracking based text cursor warping
R Biedert, A Dengel, C Käding
Proceedings of the Symposium on Eye Tracking Research and Applications, 361-364, 2012
32012
Keeping the Human in the Loop: Towards Automatic Visual Monitoring in Biodiversity Research
J Denzler, C Käding, CA Brust
10th International Conference on Ecological Informatics (ICEI), 16, 2018
22018
Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation
C Käding, J Runge
Journal of Machine Learning Research 24 (278), 1-144, 2023
12023
Human-in-the-loop: Lifelong Learning for Shallow and Deep Models
C Käding
Friedrich-Schiller-Universität Jena, 2020
12020
A Benchmark for Bivariate Causal Discovery Methods
C Käding, J Runge
EGU General Assembly Conference Abstracts, EGU21-8584, 2021
2021
Comparing Causal Discovery Methods using Synthetic and Real Data
C Käding, J Runge
EGU General Assembly 2020, 2020
2020
You have to look more than once: active and continuous exploration using YOLO
CA Brust, C Käding, J Denzler
International Conference on Computer Vision and Pattern Recognition 2017 …, 2017
2017
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