Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"

Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832

Dropout As A Bayesian Approximation: Code

Code for the ICCV 2019 paper "Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation"

Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

This repository reimplemented "MC Dropout" by tensorflow 2.0 Eager Extension.

What My Deep Model Doesn't Know...

Neural Bootstrapper (NeuBoots)

Comparison of uncertainty estimation characteristics between Bayesian neural network based on dropout, Gaussian progress regression, and density network.

Pytorch implementation of "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?"