Search Results for author:
Found 11 papers, 8 papers with code
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data.
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy?
A Complete Characterisation of ReLU-Invariant Distributions
We prove that no invariant parametrised family of distributions can exist unless at least one of the following three restrictions holds: First, the network layers have a width of one, which is unreasonable for practical neural networks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Near-Exact Recovery for Sparse-View CT via Data-Driven Methods
This work presents an empirical study on the design and training of iterative neural networks for image reconstruction from tomographic measurements with unknown geometry.
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
We study the effects of constrained optimization formulations and Frank-Wolfe algorithms for obtaining interpretable neural network predictions.
AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry
This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable").
Solving Inverse Problems With Deep Neural Networks -- Robustness Included?
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems.
Interval Neural Networks as Instability Detectors for Image Reconstructions
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks.
Interval Neural Networks: Uncertainty Scores
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.
A Rate-Distortion Framework for Explaining Neural Network Decisions
We formalise the widespread idea of interpreting neural network decisions as an explicit optimisation problem in a rate-distortion framework.
The Oracle of DLphi
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
