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Found 19 papers, 10 papers with code
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
A suite of performance metrics is employed to assess the efficacy of these methods.
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems
Denoising diffusion models have emerged as the go-to framework for solving inverse problems in imaging.
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography.
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless.
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
In this work, we propose a fast and accurate method to reconstruct activations of classification and semantic segmentation networks by stitching them with a GAN generator utilizing a 1x1 convolution.
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems
Recent years have witnessed a growth in mathematics for deep learning--which seeks a deeper understanding of the concepts of deep learning with mathematics and explores how to make it more robust--and deep learning for mathematics, where deep learning algorithms are used to solve problems in mathematics.
SELTO: Sample-Efficient Learned Topology Optimization
Recent developments in Deep Learning (DL) suggest a vast potential for Topology Optimization (TO).
PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications.
Conditional Invertible Neural Networks for Medical Imaging
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems.
Ground Truth Free Denoising by Optimal Transport
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals.
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 nonlinear magnetization behavior of (super-)paramagnetic nanoparticles to obtain a space- and often also time-dependent concentration of a tracer consisting of these nanoparticles.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing.
A Projectional Ansatz to Reconstruction
Recently the field of inverse problems has seen a growing usage of mathematically only partially understood learned and non-learned priors.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts.
Invariance and Inverse Stability under ReLU
We flip the usual approach to study invariance and robustness of neural networks by considering the non-uniqueness and instability of the inverse mapping.
Regularization by architecture: A deep prior approach for inverse problems
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems.
Singular Values for ReLU Layers
By presenting on the one hand theoretical justifications, results, and interpretations of these two concepts and on the other hand numerical experiments and results of the ReLU singular values and the Gaussian mean width being applied to trained neural networks, we hope to give a comprehensive, singular-value-centric view of ReLU layers.
A Survey on Surrogate Approaches to Non-negative Matrix Factorization
Motivated by applications in hyperspectral imaging we investigate methods for approximating a high-dimensional non-negative matrix $\mathbf{\mathit{Y}}$ by a product of two lower-dimensional, non-negative matrices $\mathbf{\mathit{K}}$ and $\mathbf{\mathit{X}}.$ This so-called non-negative matrix factorization is based on defining suitable Tikhonov functionals, which combine a discrepancy measure for $\mathbf{\mathit{Y}}\approx\mathbf{\mathit{KX}}$ with penalty terms for enforcing additional properties of $\mathbf{\mathit{K}}$ and $\mathbf{\mathit{X}}$.
Deep Learning for Tumor Classification in Imaging Mass Spectrometry
Deep learning offers an approach to learn feature extraction and classification combined in a single model.
