Alternatives and similar repositories for evidential-deep-learning-to-quantify-classification-uncertainty

Users that are interested in evidential-deep-learning-to-quantify-classification-uncertainty are comparing it to the libraries listed below

• KaosEngineer / PriorNetworks-OLD
  ☆42Updated 6 years ago
• TUM-DAML / dbu-robustness
  Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable ? (ICML 2021)
  ☆28Updated 2 years ago
• sungyubkim / MCDO
  A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
  ☆57Updated 6 years ago
• belaalb / G2DM
  Generalizing to unseen domains via distribution matching
  ☆72Updated 5 years ago
• deebuls / deep_evidential_regression_loss_pytorch
  Implementation of Deep evidential regression paper
  ☆58Updated 4 years ago
• IntelLabs / AVUC
  Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
  ☆55Updated 2 years ago
• zhang64-llnl / Mix-n-Match-Calibration
  ☆38Updated 4 years ago
• KaosEngineer / PriorNetworks
  ☆66Updated 5 years ago
• omegafragger / DDU
  Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty
  ☆144Updated 2 years ago
• csm9493 / UCL
  The implementation code for Uncertainty-based Continual Learning with Adaptive Regularization (Neurips 2019)
  ☆35Updated 4 years ago
• icml-mcbn / mcbn
  ☆53Updated 7 years ago
• ShellingFord221 / My-implementation-of-What-Uncertainties-Do-We-Need-in-Bayesian-Deep-Learning-for-Computer-Vision
  Pytorch implementation of classification task in What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision (simple vers…
  ☆79Updated 4 years ago
• markus93 / NN_calibration
  Calibration of Convolutional Neural Networks
  ☆170Updated 2 years ago
• y0ast / deterministic-uncertainty-quantification
  Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
  ☆272Updated 3 years ago
• sharpenb / Posterior-Network
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)
  ☆78Updated 3 years ago
• giannifranchi / LP_BNN
  This repository contains an official implementation of LPBNN.
  ☆38Updated 2 years ago
• bat-sheva / Conformal-Learning
  ☆25Updated 3 years ago
• inspire-group / SSD
  SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]
  ☆137Updated 4 years ago
• valeoai / ConfidNet
  Addressing Failure Prediction by Learning Model Confidence
  ☆173Updated 2 years ago
• SaynaEbrahimi / UCB
  Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020
  ☆75Updated 4 years ago
• acl21 / deep-active-learning-pytorch
  A PyTorch toolkit with 8 popular deep active learning query methods implemented.
  ☆90Updated 4 years ago
• henrikmarklund / arm
  ☆83Updated last year
• stanislavfort / exploring_the_limits_of_OOD_detection
  Code to replicate the key results from Exploring the Limits of Out-of-Distribution Detection (https://arxiv.org/abs/2106.03004) by Stanis…
  ☆44Updated 3 years ago
• amrit110 / Active-Learning-Papers
  A list of papers on Active Learning and Uncertainty Estimation for Neural Networks.
  ☆66Updated 5 years ago
• ondrejbohdal / meta-calibration
  Official PyTorch implementation of "Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error"
  ☆36Updated 2 years ago
• wiseodd / last_layer_laplace
  Last-layer Laplace approximation code examples
  ☆84Updated 3 years ago
• yliess86 / BayeFormers
  General API for Deep Bayesian Variational Inference by Backpropagation. The repository has been designed to work with Transformers like a…
  ☆45Updated 5 years ago
• jfc43 / robust-ood-detection
  Robust Out-of-distribution Detection in Neural Networks
  ☆73Updated 3 years ago
• tayden / ood-metrics
  Metrics for out-of-distribution (OOD) detection performance evaluation
  ☆49Updated 8 months ago
• rpinsler / active-bayesian-coresets
  ☆40Updated 5 years ago