Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Schmidt, Frank R."'
As deep learning models continue to advance and are increasingly utilized in real-world systems, the issue of robustness remains a major challenge. Existing certified training methods produce models that achieve high provable robustness guarantees at
Externí odkaz:
http://arxiv.org/abs/2307.13078
Many challenges from natural world can be formulated as a graph matching problem. Previous deep learning-based methods mainly consider a full two-graph matching setting. In this work, we study the more general partial matching problem with multi-grap
Externí odkaz:
http://arxiv.org/abs/2212.00780
Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have focused primarily on defending against adversarial examples for image
Externí odkaz:
http://arxiv.org/abs/2007.00147
Publikováno v:
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3896-3904, 2019
Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in al
Externí odkaz:
http://arxiv.org/abs/1904.00759
A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works have focused primarily on threat models defined by $\ell_p$ norm-bounded
Externí odkaz:
http://arxiv.org/abs/1902.07906
Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in
Externí odkaz:
http://arxiv.org/abs/1805.12514
Autor:
Laude, Emanuel, Lange, Jan-Hendrik, Schüpfer, Jonas, Domokos, Csaba, Leal-Taixé, Laura, Schmidt, Frank R., Andres, Bjoern, Cremers, Daniel
This paper introduces a novel algorithm for transductive inference in higher-order MRFs, where the unary energies are parameterized by a variable classifier. The considered task is posed as a joint optimization problem in the continuous classifier pa
Externí odkaz:
http://arxiv.org/abs/1705.05020
Publikováno v:
CVPR 2017
We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matchin
Externí odkaz:
http://arxiv.org/abs/1611.05241
We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface. We cast the problem as finding
Externí odkaz:
http://arxiv.org/abs/1601.06070
High-order (non-linear) functionals have become very popular in segmentation, stereo and other computer vision problems. Level sets is a well established general gradient descent framework, which is directly applicable to optimization of such functio
Externí odkaz:
http://arxiv.org/abs/1311.2102