mlsanityData to 14 Oct 2026

Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview

arXiv 2402.12072 ↗First posted 19 Feb 2024eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 147 of its 205 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

147 of 205 references.

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  1. 2026A neural operator view on U-Nets for inverse imaging problemscited by 0