David Stutz
TitleCited byYear
Superpixels: An evaluation of the state-of-the-art
D Stutz, A Hermans, B Leibe
Computer Vision and Image Understanding 166, 1-27, 2018
1432018
Understanding convolutional neural networks
D Stutz
Seminar Report, Visual Computing Institute, RWTH Aachen University, 2014
74*2014
Learning 3d shape completion from laser scan data with weak supervision
D Stutz, A Geiger
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
252018
Superpixel segmentation: an evaluation
D Stutz
German Conference on Pattern Recognition, 555-562, 2015
252015
Superpixel segmentation using depth information
D Stutz
RWTH Aachen University, Aachen, Germany, 2014
212014
Learning 3D Shape Completion Under Weak Supervision
D Stutz, A Geiger
International Journal of Computer Vision, 2018
132018
Disentangling adversarial robustness and generalization
D Stutz, M Hein, B Schiele
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
122019
Introduction to Neural Networks
D Stutz
Seminar Report, Human Language Technology and Pattern Recognition Group …, 2014
52014
Learning Shape Completion from Bounding Boxes with CAD Shape Priors
D Stutz
RWTH Aachen University, 2017
42017
Neural Codes for Image Retrieval
D Stutz
Seminar Report, Visual Computing Institute, RWTH Aachen University, 2015
22015
Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training
D Stutz, M Hein, B Schiele
arXiv preprint arXiv:1910.06259, 2019
2019
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
D Stutz
Seminar Report, Aachen Institute for Advanced Study in Computational …, 2016
2016
Supplementary Material for Disentangling Adversarial Robustness and Generalization
D Stutz, M Hein, B Schiele
Supplementary Material for Learning 3D Shape Completion from Laser Scan Data with Weak Supervision
D Stutz, A Geiger
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Articles 1–14