Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
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Updated
Jun 22, 2021 - Python
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
Simple and efficient way of performing deep ensembling to improve robustness as well as estimate uncertainty
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.
Code for "Deal: Deep Evidential Active Learning for Image Classification" (ICMLA 2020)
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
An implementation of natural parameter networks and its extension to GRUs in PyTorch
A neural-network based image classifier that quantifies its uncertainty using Bayesian methods, as described in Kendall and Gal (2017)
Behaviour Cloning of Cartpole Swing-up Policy with Model-Predictive Uncertainty Regularization (UW CSE571 Guided Project)
Official Code: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
Attempt to reproduce the toy experiment of http://bit.ly/2C9Z8St with an ensemble of nets and with dropout.
Uncertainty aware brain age prediction
Experiments from Efficient Training of Interval Neural Networks for Imprecise Training Data
Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf
This repository provides the official implementation of "Robust channel-wise illumination estimation." accepted in BMVC (2021).
Inferring distributions over depth from a single image, IROS 2019
PyTorch implementation of Probabilistic MIMO U-Net
Code and supporting materials for the ICLR 2020 RIO paper
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.
A CNN based Depth, Optical Flow, Flow Uncertainty and Camera Pose Prediction pipeline
[WACV'22] Official implementation of "HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty"
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