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David Stutz, Abhijit Guha Roy, Tatiana Matejovicova, Patricia Strachan, Ali Taylan Cemgil, Arnaud Doucet.
Conformal prediction under ambiguous ground truth.
ArXiv, 2023.
[ArXiv | Project Page]
David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy, Tatiana Matejovicova, Melih Barsbey, Patricia Strachan, Mike Schaekermann, Jan Freyberg, Rajeev Rikhye, Beverly Freeman, Javier Perez Matos, Umesh Telang, Dale R. Webster, Yuan Liu, Greg S. Corrado, Yossi Matias, Pushmeet Kohli, Yun Liu, Arnaud Doucet, Alan Karthikesalingam.
Evaluating AI systems under uncertain ground truth: a case study in dermatology.
ArXiv, 2023.
[ArXiv | Project Page]
David Stutz, Krishnamurthy (Dj) Dvijotham, Ali Taylan Cemgil, Arnaud Doucet.
Learning Optimal Conformal Classifiers.
ICLR, 2022.
[ArXiv | OpenReview | Project Page]
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele.
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators.
TPAMI, 2022.
[ArXiv | IEEExplore | Project Page]
David Stutz, Matthias Hein, Bernt Schiele.
Relating Adversarially Robust Generalization to Flat Minima.
ICCV, 2021.
[ArXiv | Project Page]
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele.
On Mitigating Random and Adversarial Bit Errors.
MLSys, 2021.
[ArXiv | BibTeX | Project Page]
David Stutz, Matthias Hein, Bernt Schiele.
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks.
ICML, 2020.
[ArXiv | BibTeX | Project Page]
David Stutz, Andreas Geiger.
Learning 3D Shape Completion under Weak Supervision.
International Journal of Computer Vision, 2020.
[DOI | ArXiv | BibTeX | Project Page]
David Stutz, Matthias Hein, Bernt Schiele.
Disentangling Adversarial Robustness and Generalization.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[ArXiv | BibTeX | Project Page]
David Stutz, Andreas Geiger.
Learning 3D Shape Completion from Laser Scan Data with Weak Supervision.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[PDF | BibTeX | Project Page]
David Stutz, Alexander Hermans, Bastian Leibe.
Superpixels: an evaluation of the state-of-the-art.
Computer Vision and Image Understanding, Volume 166, 2018.
[DOI | ArXiv | PDF | BibTeX | Project Page]
David Stutz.
Superpixel segmentation: an evaluation.
German Conference on Pattern Recognition, 2015.
[PDF | BibTeX | Project Page]
A comparison of several state-of-the-art superpixel algorithms using an extended version of the Berkeley Segmentation Benchmark.