David Stutz

Eyke Hüllermeier

CoRR, 2024

On Adversarial Training without Perturbing all Examples.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators.

[BibT_eX]

[DOI]

Nandhini Chandramoorthy

IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

Conformal prediction under ambiguous ground truth.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Unlocking Accuracy and Fairness in Differentially Private Image Classification.

[BibT_eX]

[DOI]

CoRR, 2023

Evaluating AI systems under uncertain ground truth: a case study in dermatology.

[BibT_eX]

[DOI]

CoRR, 2023

Robustifying Token Attention for Vision Transformers.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Certified Robust Models with Slack Control and Large Lipschitz Constants.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition - 45th DAGM German Conference, 2023

Improving Robustness of Vision Transformers by Reducing Sensitivity to Patch Corruptions.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Understanding and improving robustness and uncertainty estimation in deep learning.

[BibT_eX]

[DOI]

PhD thesis, 2022

On Fragile Features and Batch Normalization in Adversarial Training.

[BibT_eX]

[DOI]

Nils Philipp Walter

CoRR, 2022

Improving Corruption and Adversarial Robustness by Enhancing Weak Subnets.

[BibT_eX]

[DOI]

CoRR, 2022

Learning Optimal Conformal Classifiers.

[BibT_eX]

[DOI]

Krishnamurthy Dvijotham

Ali Taylan Cemgil

Arnaud Doucet

Proceedings of the Tenth International Conference on Learning Representations, 2022

Improving Robustness by Enhancing Weak Subnets.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision, 2022

2021

A Closer Look at the Adversarial Robustness of Information Bottleneck Models.

[BibT_eX]

[DOI]

CoRR, 2021

Bit Error Robustness for Energy-Efficient DNN Accelerators.

[BibT_eX]

[DOI]

Nandhini Chandramoorthy

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

Relating Adversarially Robust Generalization to Flat Minima.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Learning 3D Shape Completion Under Weak Supervision.

[BibT_eX]

[DOI]

Andreas Geiger

Int. J. Comput. Vis., 2020

On Mitigating Random and Adversarial Bit Errors.

[BibT_eX]

[DOI]

Nandhini Chandramoorthy

CoRR, 2020

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Adversarial Training Against Location-Optimized Adversarial Patches.

[BibT_eX]

[DOI]

Sukrut Rao

Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

2019

Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training.

[BibT_eX]

[DOI]

CoRR, 2019

Disentangling Adversarial Robustness and Generalization.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Superpixels: An evaluation of the state-of-the-art.

[BibT_eX]

[DOI]

Alexander Hermans

Bastian Leibe

Comput. Vis. Image Underst., 2018

Learning 3D Shape Completion From Laser Scan Data With Weak Supervision.

[BibT_eX]

[DOI]

Andreas Geiger

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2015

Superpixel Segmentation: An Evaluation.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition - 37th German Conference, 2015

2003

Shared source CLI essentials - exploring Microsoft's Rotor and the ECMA CLI.

[BibT_eX]

[DOI]