Uncertainty Quantification with Generative Models
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Abstract (arXiv)
We develop a generative model-based approach to Bayesian inverse problems, such as image reconstruction from noisy and incomplete images. Our framework addresses two common challenges of Bayesian reconstructions: 1) It makes use of complex, data-driven priors that comprise all available information about the uncorrupted data distribution. 2) It enables computationally tractable uncertainty quantification in the form of posterior analysis in latent and data space. The method is very efficient in that the generative model only has to be trained once on an uncorrupted data set, after that, the procedure can be used for arbitrary corruption types.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2019.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 3; 2025: 4; 2026: 3.
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- 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
- 2021PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physicscited by 21
- 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Modelscited by 13
- 2021Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustabilitycited by 11
- 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imagingcited by 10
- 2021Dense Uncertainty Estimationcited by 9
- 2022PCENet: High Dimensional Surrogate Modeling for Learning Uncertaintycited by 5
- 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imagingcited by 4
- 2024Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagationcited by 3
- 2025Topology-Aware Conformal Prediction for Stream Networkscited by 3
- 2024An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Unitscited by 2
- 2026Quantifying Epistemic Uncertainty in Diffusion Modelscited by 2
5 more
- 2021Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoderscited by 1
- 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspectivecited by 1
- 2026Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantificationcited by 1
- 2026LatentFlow: A General Framework for Conditioning Stochastic Processescited by 1
- 2025Actively Inferring Optimal Measurement Sequencescited by 0
- 2026LatentFlow: A General Framework for Conditioning Stochastic Processes14 Jul 2026
- 2026Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification30 Jun 2026
- 2026Quantifying Epistemic Uncertainty in Diffusion Models9 Feb 2026
- 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective13 Jul 2025
- 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging10 Apr 2025
- 2025Topology-Aware Conformal Prediction for Stream Networks6 Mar 2025
- 2025Actively Inferring Optimal Measurement Sequences25 Feb 2025
- 2024An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Units19 Jul 2024
- 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models23 May 2024
- 2024Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation31 Mar 2024
- 2023Synthetic data, real errors: how (not) to publish and use synthetic data16 May 2023
- 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imaging2 Jul 2022
5 more
- 2022PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty10 Feb 2022
- 2021Dense Uncertainty Estimation13 Oct 2021
- 2021PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics6 Jun 2021
- 2021Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders27 May 2021
- 2021Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability25 May 2021
- 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Modelscited by 13
- 2026Quantifying Epistemic Uncertainty in Diffusion Modelscited by 2
- 2026Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantificationcited by 1
- 2026Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification30 Jun 2026
- 2026Quantifying Epistemic Uncertainty in Diffusion Models9 Feb 2026
- 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models23 May 2024
- 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
- 2021PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physicscited by 21
- 2021Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustabilitycited by 11
- 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imagingcited by 10
- 2021Dense Uncertainty Estimationcited by 9
- 2022PCENet: High Dimensional Surrogate Modeling for Learning Uncertaintycited by 5
- 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imagingcited by 4
- 2024Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagationcited by 3
- 2025Topology-Aware Conformal Prediction for Stream Networkscited by 3
- 2024An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Unitscited by 2
- 2021Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoderscited by 1
- 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspectivecited by 1
12 most cited of 14 All 14 (JSON)
- 2026LatentFlow: A General Framework for Conditioning Stochastic Processes14 Jul 2026
- 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective13 Jul 2025
- 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging10 Apr 2025
- 2025Topology-Aware Conformal Prediction for Stream Networks6 Mar 2025
- 2025Actively Inferring Optimal Measurement Sequences25 Feb 2025
- 2024An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Units19 Jul 2024
- 2024Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation31 Mar 2024
- 2023Synthetic data, real errors: how (not) to publish and use synthetic data16 May 2023
- 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imaging2 Jul 2022
- 2022PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty10 Feb 2022
- 2021Dense Uncertainty Estimation13 Oct 2021
- 2021PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics6 Jun 2021
12 newest of 14 All 14 (JSON)