[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
Finite versus infinite neural networks: an empirical study
We perform a careful, thorough, and large scale empirical study of the correspondence
between wide neural networks and kernel methods. By doing so, we resolve a variety of …
between wide neural networks and kernel methods. By doing so, we resolve a variety of …
DeepRED: Deep image prior powered by RED
G Mataev, P Milanfar, M Elad - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and
theory that have been accumulated over the years. Recently, this field has been immensely …
theory that have been accumulated over the years. Recently, this field has been immensely …
Computed tomography reconstruction using deep image prior and learned reconstruction methods
In this paper we describe an investigation into the application of deep learning methods for
low-dose and sparse angle computed tomography using small training datasets. To motivate …
low-dose and sparse angle computed tomography using small training datasets. To motivate …
Noise2inverse: Self-supervised deep convolutional denoising for tomography
AA Hendriksen, DM Pelt… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Recovering a high-quality image from noisy indirect measurements is an important problem
with many applications. For such inverse problems, supervised deep convolutional neural …
with many applications. For such inverse problems, supervised deep convolutional neural …
Early stopping for deep image prior
Deep image prior (DIP) and its variants have showed remarkable potential for solving
inverse problems in computer vision, without any extra training data. Practical DIP models …
inverse problems in computer vision, without any extra training data. Practical DIP models …
Rethinking deep image prior for denoising
Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems.
Among them, denoising is known to be particularly challenging for the DIP due to noise …
Among them, denoising is known to be particularly challenging for the DIP due to noise …
Diffraction tomography with a deep image prior
KC Zhou, R Horstmeyer - Optics express, 2020 - opg.optica.org
We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography
(DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high …
(DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high …
Untrained neural network priors for inverse imaging problems: A survey
In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …