Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
<|reference_start|>Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview: 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.<|reference_end|>
@article{auras2024robustness,
    title={Robustness and Exploration of Variational and Machine Learning
  Approaches to Inverse Problems: An Overview},
    author={Alexander Auras, Kanchana Vaishnavi Gandikota, Hannah Droege, Michael
  Moeller},
    journal={arXiv preprint arXiv:2402.12072},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12072},
    primaryClass={eess.IV cs.CV cs.LG cs.NA math.NA}
}