David Stutz
Title
Cited by
Cited by
Year
Superpixels: An evaluation of the state-of-the-art
D Stutz, A Hermans, B Leibe
Computer Vision and Image Understanding 166, 1-27, 2018
2242018
Understanding convolutional neural networks
D Stutz
Seminar Report, Visual Computing Institute, RWTH Aachen University, 2014
108*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
602018
Disentangling adversarial robustness and generalization
D Stutz, M Hein, B Schiele
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
482019
Superpixel segmentation: an evaluation
D Stutz
German conference on pattern recognition, 555-562, 2015
272015
Learning 3D Shape Completion Under Weak Supervision
D Stutz, A Geiger
International Journal of Computer Vision, 2018
242018
Superpixel segmentation using depth information
D Stutz
RWTH Aachen University, Aachen, Germany, 2014
222014
Introduction to Neural Networks
D Stutz
Seminar Report, Human Language Technology and Pattern Recognition Group …, 2014
62014
Learning Shape Completion from Bounding Boxes with CAD Shape Priors
D Stutz
RWTH Aachen University, 2017
52017
Confidence-calibrated adversarial training and detection: More robust models generalizing beyond the attack used during training
D Stutz, M Hein, B Schiele
arXiv preprint arXiv:1910.06259, 2019
32019
Adversarial Training against Location-Optimized Adversarial Patches
S Rao, D Stutz, B Schiele
arXiv preprint arXiv:2005.02313, 2020
22020
Confidence-calibrated adversarial training: Generalizing to unseen attacks
D Stutz, M Hein, B Schiele
Proc. of the International Conference on Machine Learning (ICML), 2020
22020
Neural Codes for Image Retrieval
D Stutz
Seminar Report, Visual Computing Institute, RWTH Aachen University, 2015
22015
On Mitigating Random and Adversarial Bit Errors
D Stutz, N Chandramoorthy, M Hein, B Schiele
arXiv preprint arXiv:2006.13977, 2020
2020
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 Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
D Stutz, M Hein, B Schiele
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 1–19