My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
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Updated
Jan 15, 2018 - Python
My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
An implementation of natural parameter networks and its extension to GRUs in PyTorch
Attempt to reproduce the toy experiment of http://bit.ly/2C9Z8St with an ensemble of nets and with dropout.
A CNN based Depth, Optical Flow, Flow Uncertainty and Camera Pose Prediction pipeline
Experiments from Efficient Training of Interval Neural Networks for Imprecise Training Data
A neural-network based image classifier that quantifies its uncertainty using Bayesian methods, as described in Kendall and Gal (2017)
Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf
Code for "Deal: Deep Evidential Active Learning for Image Classification" (ICMLA 2020)
This repository provides the code used to implement the framework to provide deep learning models with total uncertainty estimates as described in "A General Framework for Uncertainty Estimation in Deep Learning" (Loquercio, Segù, Scaramuzza. RA-L 2020).
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
This repository provides the official implementation of "Robust channel-wise illumination estimation." accepted in BMVC (2021).
[WACV'22] Official implementation of "HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty"
Official Code: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Wasserstein dropout (W-dropout) is a novel technique to quantify uncertainty in regression networks. It is fully non-parametric and yields accurate uncertainty estimates - even under data shifts.
A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
Uncertainty aware brain age prediction
Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
NOMU: Neural Optimization-based Model Uncertainty
Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.
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