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08/28/2023
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems
Denoising diffusion models have emerged as the go-to framework for solvi...
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08/27/2023
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results...
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07/09/2023
Parameter Identification by Deep Learning of a Material Model for Granular Media
Classical physical modelling with associated numerical simulation (model...
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06/02/2023
Invertible residual networks in the context of regularization theory for linear inverse problems
Learned inverse problem solvers exhibit remarkable performance in applic...
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03/28/2023
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
The deep image prior (DIP) is a well-established unsupervised deep learn...
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02/04/2023
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
Critical applications, such as in the medical field, require the rapid p...
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12/06/2022
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems
Recent years have witnessed a growth in mathematics for deep learning–wh...
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09/12/2022
SELTO: Sample-Efficient Learned Topology Optimization
We present a sample-efficient deep learning strategy for topology optimi...
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05/24/2022
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization
Learning neural networks using only a small amount of data is an importa...
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12/14/2021
Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering
In this work, we focus on connections between K-means clustering approac...
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07/03/2020
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset
Magnetic particle imaging (MPI) is an imaging modality exploiting the no...
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07/03/2020
Ground Truth Free Denoising by Optimal Transport
We present a learned unsupervised denoising method for arbitrary types o...
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04/27/2020
Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to ma
One important property of imaging modalities and related applications is...
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04/14/2020
Error analysis for filtered back projection reconstructions in Besov spaces
Filtered back projection (FBP) methods are the most widely used reconstr...
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12/10/2019
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Neural networks have recently been established as a viable classificatio...
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10/01/2019
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
Deep Learning approaches for solving Inverse Problems in imaging have be...
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07/10/2019
A Projectional Ansatz to Reconstruction
Recently the field of inverse problems has seen a growing usage of mathe...
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05/10/2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Recent studies on the adversarial vulnerability of neural networks have ...
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12/10/2018
Regularization by architecture: A deep prior approach for inverse problems
The present paper studies the so called deep image prior (DIP) technique...
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12/06/2018
Singular Values for ReLU Layers
Despite their prevalence in neural networks we still lack a thorough the...
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08/06/2018
A Survey on Surrogate Approaches to Non-negative Matrix Factorization
Motivated by applications in hyperspectral imaging we investigate method...
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06/25/2018
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Studying the invertibility of deep neural networks (DNNs) provides a pri...
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05/02/2017
