| Incremental learning algorithms and applications A Gepperth, B Hammer | 452 | 2016 |
| A bio-inspired incremental learning architecture for applied perceptual problems A Gepperth, C Karaoguz Cognitive Computation 8 (5), 924-934, 2016 | 187 | 2016 |
| A comprehensive, application-oriented study of catastrophic forgetting in DNNs B Pfülb, A Gepperth International Conference on Learning Representations (ICLR), 2019 | 104 | 2019 |
| Behavior prediction at multiple time-scales in inner-city scenarios MG Ortiz, J Fritsch, F Kummert, A Gepperth 2011 IEEE Intelligent Vehicles Symposium (IV), 1068-1073, 2011 | 73 | 2011 |
| A multi-modal system for road detection and segmentation X Hu, FSA Rodriguez, A Gepperth 2014 IEEE Intelligent Vehicles Symposium Proceedings, 1365-1370, 2014 | 68 | 2014 |
| RGBD object recognition and visual texture classification for indoor semantic mapping D Filliat, E Battesti, S Bazeille, G Duceux, A Gepperth, L Harrath, I Jebari, ... 2012 IEEE international conference on technologies for practical robot …, 2012 | 67 | 2012 |
| Predicting network flow characteristics using deep learning and real-world network traffic C Hardegen, B Pfülb, S Rieger, A Gepperth IEEE Transactions on Network and Service Management 17 (4), 2662-2676, 2020 | 58 | 2020 |
| A comparison of geometric and energy-based point cloud semantic segmentation methods M Dubois, PK Rozo, A Gepperth, OFA González, D Filliat 2013 European Conference on Mobile Robots, 88-93, 2013 | 51 | 2013 |
| A study on catastrophic forgetting in deep LSTM networks M Schak, A Gepperth Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …, 2019 | 50 | 2019 |
| Towards a human-like vision system for driver assistance J Fritsch, T Michalke, A Gepperth, S Bone, F Waibel, M Kleinehagenbrock, ... 2008 IEEE intelligent vehicles symposium, 275-282, 2008 | 43 | 2008 |
| Real-time detection and classification of cars in video sequences A Gepperth, J Edelbrunner, T Bucher IEEE Proceedings. Intelligent Vehicles Symposium, 2005., 625-631, 2005 | 43 | 2005 |
| Marginal replay vs conditional replay for continual learning T Lesort, A Gepperth, A Stoian, D Filliat International Conference on Artificial Neural Networks, 466-480, 2019 | 39 | 2019 |
| Gradient-based training of gaussian mixture models for high-dimensional streaming data A Gepperth, B Pfülb Neural Processing Letters 53 (6), 4331-4348, 2021 | 33* | 2021 |
| Dynamic hand gesture recognition for mobile systems using deep LSTM A Sarkar, A Gepperth, U Handmann, T Kopinski Intelligent Human Computer Interaction: 9th International Conference, IHCI …, 2017 | 33 | 2017 |
| Applications of multi-objective structure optimization A Gepperth, S Roth Neurocomputing 69 (7-9), 701-713, 2006 | 32 | 2006 |
| An investigation of replay-based approaches for continual learning B Bagus, A Gepperth 2021 International Joint Conference on Neural Networks (IJCNN), 1-9, 2021 | 31 | 2021 |
| Flow-based throughput prediction using deep learning and real-world network traffic C Hardegen, B Pfülb, S Rieger, A Gepperth, S Reißmann 2019 15th International Conference on Network and Service Management (CNSM), 1-9, 2019 | 28 | 2019 |
| Catastrophic forgetting: still a problem for DNNs B Pfülb, A Gepperth, S Abdullah, A Kilian Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 28 | 2018 |
| Continual learning: Applications and the road forward E Verwimp, R Aljundi, S Ben-David, M Bethge, A Cossu, A Gepperth, ... arXiv preprint arXiv:2311.11908, 2023 | 26 | 2023 |
| A study of deep learning for network traffic data forecasting B Pfülb, C Hardegen, A Gepperth, S Rieger Artificial Neural Networks and Machine Learning–ICANN 2019: Text and Time …, 2019 | 25 | 2019 |
