Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929414591217664 |
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| author | Auras, Alexander Gandikota, Kanchana Vaishnavi Droege, Hannah Moeller, Michael |
| author_facet | Auras, Alexander Gandikota, Kanchana Vaishnavi Droege, Hannah Moeller, Michael |
| contents | This paper provides an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one-dimensional toy problem are provided, showing the robustness of different approaches and empirically verifying theoretical guarantees. Another focus of this review is the exploration of the subspace of data-consistent solutions through explicit guidance to satisfy specific semantic or textural properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview Auras, Alexander Gandikota, Kanchana Vaishnavi Droege, Hannah Moeller, Michael Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Numerical Analysis This paper provides an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one-dimensional toy problem are provided, showing the robustness of different approaches and empirically verifying theoretical guarantees. Another focus of this review is the exploration of the subspace of data-consistent solutions through explicit guidance to satisfy specific semantic or textural properties. |
| title | Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2402.12072 |