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On the Quantification of Image Reconstruction Uncertainty without
  Training Data

On the Quantification of Image Reconstruction Uncertainty without Training Data

16 November 2023
Sirui Bi
Victor Fung
Jiaxin Zhang
ArXivPDFHTML

Papers citing "On the Quantification of Image Reconstruction Uncertainty without Training Data"

6 / 6 papers shown
Title
Simulation Intelligence: Towards a New Generation of Scientific Methods
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin
D. Krakauer
Hector Zenil
Justin Emile Gottschlich
Tim Mattson
...
A. Hanuka
Manuela Veloso
Samuel A. Assefa
Stephan Zheng
Avi Pfeffer
40
104
0
06 Dec 2021
Intermediate Layer Optimization for Inverse Problems using Deep
  Generative Models
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
Giannis Daras
Joseph Dean
A. Jalal
A. Dimakis
DRL
183
83
0
15 Feb 2021
Solving Inverse Problems with a Flow-based Noise Model
Solving Inverse Problems with a Flow-based Noise Model
Jay Whang
Qi Lei
A. Dimakis
56
36
0
18 Mar 2020
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan
Didrik Nielsen
Voot Tangkaratt
Wu Lin
Y. Gal
Akash Srivastava
ODL
74
266
0
13 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,134
0
06 Jun 2015
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