atilberk / evidential-deep-learning-to-quantify-classification-uncertaintyLinks
Work on Evidential Deep Learning to Quantify Classification Uncertainty
☆60Updated 6 years ago
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
Sorting:
- ☆42Updated 6 years ago
- Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable ? (ICML 2021)☆27Updated 2 years ago
- A pytorch implementation of MCDO(Monte-Carlo Dropout methods)☆56Updated 6 years ago
- Implementation of Deep evidential regression paper☆57Updated 4 years ago
- Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.☆54Updated 2 years ago
- ☆66Updated 5 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆143Updated 2 years ago
- This repository contains an official implementation of LPBNN.☆38Updated 2 years ago
- Generalizing to unseen domains via distribution matching☆72Updated 4 years ago
- ☆37Updated 4 years ago
- Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020☆50Updated 4 years ago
- Robust Out-of-distribution Detection in Neural Networks☆73Updated 3 years ago
- ☆53Updated 7 years ago
- General API for Deep Bayesian Variational Inference by Backpropagation. The repository has been designed to work with Transformers like a…☆45Updated 4 years ago
- ShellingFord221 / My-implementation-of-What-Uncertainties-Do-We-Need-in-Bayesian-Deep-Learning-for-Computer-VisionPytorch implementation of classification task in What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision (simple vers…☆79Updated 4 years ago
- ☆25Updated 3 years ago
- Addressing Failure Prediction by Learning Model Confidence☆173Updated 2 years ago
- SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]☆137Updated 4 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆77Updated 3 years ago
- Official PyTorch implementation of "Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error"☆36Updated last year
- Last-layer Laplace approximation code examples☆82Updated 3 years ago
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"☆272Updated 3 years ago
- Out-of-distribution detection using the pNML regret. NeurIPS2021☆25Updated 7 months ago
- ☆46Updated 4 years ago
- Calibration of Convolutional Neural Networks☆168Updated 2 years ago
- Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization☆87Updated 3 years ago
- ☆84Updated last year
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆160Updated last year
- An official PyTorch implementation of "Regression Prior Networks" for effective runtime uncertainty estimation.☆36Updated 4 years ago
- An implementation of the Residual Flow algorithm for out-of-distribution detection.☆31Updated 3 years ago