| 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 | 150 | 2016 |
| Active Learning for Deep Object Detection CA Brust, C Käding, J Denzler arXiv preprint arXiv:1809.09875, 2018 | 147 | 2018 |
| 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 | 94 | 2017 |
| 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 | 70 | 2015 |
| 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 | 68 | 2016 |
| Active Learning for Regression Tasks with Expected Model Output Changes C Käding, E Rodner, A Freytag, O Mothes, B Barz, J Denzler | 40 | 2018 |
| Active and incremental learning with weak supervision CA Brust, C Käding, J Denzler KI-Künstliche Intelligenz 34 (2), 165-180, 2020 | 27 | 2020 |
| 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 | 25 | 2017 |
| 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 | 24 | 2016 |
| 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 | 16 | 2022 |
| Information-theoretic active learning for content-based image retrieval B Barz, C Käding, J Denzler German Conference on Pattern Recognition, 650-666, 2018 | 16 | 2018 |
| 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 | 14 | 2016 |
| 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 | 4 | 2017 |
| 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 | 3 | 2012 |
| 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 | 2 | 2023 |
| 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 | 2 | 2018 |
| Human-in-the-loop: Lifelong Learning for Shallow and Deep Models C Käding Friedrich-Schiller-Universität Jena, 2020 | 1 | 2020 |
| 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 |
