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pro vyhledávání: '"Lunz, Sebastian"'
Autor:
Mukherjee, Subhadip, Dittmer, Sören, Shumaylov, Zakhar, Lunz, Sebastian, Öktem, Ozan, Schönlieb, Carola-Bibiane
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional. The ICNN-based convex regularizer is trained adversarially to discern
Externí odkaz:
http://arxiv.org/abs/2008.02839
Autor:
Lunz, Sebastian, Hauptmann, Andreas, Tarvainen, Tanja, Schönlieb, Carola-Bibiane, Arridge, Simon
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions. This paper discusses the conceptual
Externí odkaz:
http://arxiv.org/abs/2005.07069
Recent work has shown the ability to learn generative models for 3D shapes from only unstructured 2D images. However, training such models requires differentiating through the rasterization step of the rendering process, therefore past work has focus
Externí odkaz:
http://arxiv.org/abs/2002.12674
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts. We aim to quantify this behavior b
Externí odkaz:
http://arxiv.org/abs/1905.04172
Autor:
Alfke, Dominik, Baines, Weston, Blechschmidt, Jan, Sarmina, Mauricio J. del Razo, Drory, Amnon, Elbrächter, Dennis, Farchmin, Nando, Gambara, Matteo, Glas, Silke, Grohs, Philipp, Hinz, Peter, Kivaranovic, Danijel, Kümmerle, Christian, Kutyniok, Gitta, Lunz, Sebastian, Macdonald, Jan, Malthaner, Ryan, Naisat, Gregory, Neufeld, Ariel, Petersen, Philipp Christian, Reisenhofer, Rafael, Sheng, Jun-Da, Thesing, Laura, Trunschke, Philipp, von Lindheim, Johannes, Weber, David, Weber, Melanie
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of labelled training data, our methodology is capable of predicting the la
Externí odkaz:
http://arxiv.org/abs/1901.05744
Publikováno v:
Inverse Problems, Vol. 38, Issue 7 (2022)
The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect is to formalize the steps of reconstruction and task as appropriate es
Externí odkaz:
http://arxiv.org/abs/1809.00948
Autor:
Adler, Jonas, Lunz, Sebastian
Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on a notion of distance between individual images, which induces a notio
Externí odkaz:
http://arxiv.org/abs/1806.06621
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most popular approaches. We propose a new framework for applying data-driv
Externí odkaz:
http://arxiv.org/abs/1805.11572
Akademický článek
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Autor:
Lunz, Sebastian
In this thesis, we investigate properties of deep neural networks and their application to inverse problems. A successful classical approach to inverse problems is variational regularisation, combining knowledge and modelling of the imaging modality
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::604d08f365f1f85e79f37b8753249527