methodology-7

Uncertainty Visualization

3260 papers • 126 benchmarks • 313 datasets

Visualize uncertainty in restoration models.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in uncertainty-visualization-21

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Libraries

i

Use these libraries to find uncertainty-visualization-21 models and implementations

Datasets

MUAD

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Most implemented papers

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images Using Bayesian Deep Learning

S. Sedai, B. Antony, D. Mahapatra, R. Garnavi•Tue Sep 11 2018

This paper proposes a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning that not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output.

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Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning

N. Navab, M. Eslami, Solale Tabarestani, Ehsan Adeli, G. Elwyn, T. Elze, Mengyu Wang, Nazlee Zebardast, M. Adjouadi•Sun May 08 2022

A first-of-its-kind estimation approach that not only can serve to assist physicians and clinicians in making reasoned medical decisions, but it also allows to appreciate the uncertainty visualization, which could raise incertitude in making the appropriate classification or prediction.

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On the Posterior Distribution in Denoising: Application to Uncertainty Quantification

T. Michaeli, Hila Manor•Sat Sep 23 2023

This work derives a fundamental relation between the higher-order central moments of the posterior distribution, and the higher-order derivatives of the posterior mean of Gaussian denoising, and harnesses this result for uncertainty quantification of pre-trained denoisers.

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Uncertainty Visualization via Low-Dimensional Posterior Projections

Elias Nehme, T. Michaeli, Omer Yair•Mon Dec 11 2023

This work introduces a new approach for estimating and visualizing posteriors by em-ploying energy-based models (EBMs) over low-dimensional subspaces over low-dimensional subspaces by training a conditional EBM that receives an input measurement and a set of directions that span some low-dimensional subspace of solutions, and outputs the probability density function of the posterior within that space.

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VOICE: Variance of Induced Contrastive Explanations to Quantify Uncertainty in Neural Network Interpretability

M. Prabhushankar, Ghassan AlRegib•Fri May 31 2024

This analysis allows for a plug in approach to visualize and quantify the remaining predictive uncertainty of any gradient-based explanatory technique, and shows that every image, network, prediction, and explanatory technique has a unique uncertainty.

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