seb5666 / cnn_gaussian_process_uncertainty
Use Gaussian processes to estimate CNN classification uncertainty
☆12Updated 6 years ago
Related projects ⓘ
Alternatives and complementary repositories for cnn_gaussian_process_uncertainty
- Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf☆30Updated 4 years ago
- Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)☆135Updated 5 years ago
- Code for "Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference" (NeurIPS Bayesian Deep Learning W…☆23Updated 4 years ago
- using monte carlo dropout to have uncertainty estimation of predictions☆14Updated 4 years ago
- Interpreting Bayesian inference as continual learning with a CNN☆22Updated 6 years ago
- My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"☆134Updated 6 years ago
- 3D Bayesian Convolutional Neural Network (BCNN) for Credible Geometric Uncertainty. Code for the paper: https://arxiv.org/abs/1910.10793☆61Updated last year
- Epistemic Uncertainty Estimation with Monte Carlo Dropout☆8Updated 5 years ago
- A pytorch implementation of MCDO(Monte-Carlo Dropout methods)☆55Updated 5 years ago
- This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble☆148Updated 2 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆139Updated 6 years ago
- Visually Explainable VAE☆62Updated 3 years ago
- This repository reimplemented "MC Dropout" by tensorflow 2.0 Eager Extension.☆16Updated last year
- Semi-supervised Regression GAN☆37Updated 4 years ago
- Dropout as Regularization and Bayesian Approximation☆56Updated 5 years ago
- General API for Deep Bayesian Variational Inference by Backpropagation. The repository has been designed to work with Transformers like a…☆41Updated 4 years ago
- Bayesian Neural Network in PyTorch☆79Updated 6 months ago
- Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable ? (ICML 2021)☆27Updated last year
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆72Updated last year
- ☆226Updated 4 years ago
- Utilities to perform Uncertainty Quantification on Keras Models☆113Updated 8 months ago
- A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.☆50Updated 4 years ago
- This repository contains an official implementation of LPBNN.☆39Updated last year
- Pytorch Bayesian UNet model for segmentation and uncertainty prediction☆19Updated 2 years ago
- The official implementation of the MC-Dropconnect method for Uncertainty Estimation in DNNs☆15Updated 4 years ago
- ☆13Updated 4 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆86Updated 4 years ago
- Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.☆132Updated 4 years ago
- Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020☆73Updated 3 years ago
- Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.☆53Updated last year