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Search results for uncertainty neural networks

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24 search results found

Bayesian Neural Networks ⭐ 1,633

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

Bayesian Torch ⭐ 396

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

Awesome Uncertainty Deeplearning ⭐ 329

This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

Pytorch Classification Uncertainty ⭐ 235

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

Uncertainty_estimation_deep_learning ⭐ 149

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).

Masksembles ⭐ 90

Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).

Deep Ensembles Uncertainty ⭐ 82

My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.

Monodepth Uncertainty ⭐ 39

Inferring distributions over depth from a single image, IROS 2019

Uncertainty Wizard ⭐ 39

Uncertainty-Wizard is a plugin on top of tensorflow.keras, allowing to easily and efficiently create uncertainty-aware deep neural networks. Also useful if you want to train multiple small models in parallel.

Nlp Uncertainty Zoo ⭐ 32

Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.

Bayesian Neural Networks Reading List ⭐ 16

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

Protein Uq ⭐ 13

Benchmarking uncertainty quantification methods on proteins.

Mc Dropout Mnist ⭐ 12

Implementation of MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_baye

Active Learning Papers ⭐ 10

A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.

Dataset and code for "Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity"

Mimo Unet ⭐ 10

PyTorch implementation of Probabilistic MIMO U-Net

A CNN based Depth, Optical Flow, Flow Uncertainty and Camera Pose Prediction pipeline

Robust Deep Learning ⭐ 9

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.

Rio Paper ⭐ 8

Code and supporting materials for the ICLR 2020 RIO paper

[WACV'22] Official implementation of "HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty"

NOMU: Neural Optimization-based Model Uncertainty

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