clabrugere / evidential-deeplearning
Implementation of "Evidential Deep Learning to Quantify Classification Uncertainty" proposing a method to quantify uncertainty in a neural network.
☆25Updated last year
Alternatives and similar repositories for evidential-deeplearning:
Users that are interested in evidential-deeplearning are comparing it to the libraries listed below
- Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments☆68Updated 5 years ago
- Evidential Deep Learning in PyTorch☆54Updated 2 years ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆139Updated last year
- An implementation of the state-of-the-art Deep Active Learning algorithms☆103Updated last year
- [ICML 2023] Offical implementation of the paper "Uncertainty Estimation by Fisher Information-based Evidential Deep Learning".☆43Updated last year
- Model-agnostic posthoc calibration without distributional assumptions☆42Updated last year
- Active and Sample-Efficient Model Evaluation☆24Updated 4 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆112Updated 2 years ago
- Local explanations with uncertainty 💐!☆40Updated last year
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆31Updated 2 years ago
- Work on Evidential Deep Learning to Quantify Classification Uncertainty☆60Updated 6 years ago
- This is reimplementation of "Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness" in Pyt…☆50Updated 3 years ago
- ☆105Updated 3 years ago
- ☆34Updated 4 years ago
- Code for experiments to learn uncertainty☆30Updated 2 years ago
- Implementation of Deep evidential regression paper☆55Updated 4 years ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆35Updated last year
- Deep Batch Active Learning for Regression☆66Updated 6 months ago
- Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vi…☆66Updated 2 years ago
- C-Mixup for NeurIPS 2022☆70Updated last year
- Official implementation of Evidential Uncertainty Quantification: A Variance-Based Perspective [WACV 2024]☆18Updated last year
- Official repository for the paper "Fast Predictive Uncertainty for Classification with Bayesian Deep Networks". Accepted at UAI 2022. htt…☆12Updated 2 years ago
- ☆25Updated 2 years ago
- The official codebase for Predictive Inference with Feature Conformal Prediction☆34Updated 2 years ago
- [ICLR 2023 Spotlight] Code release for "Dirichlet-based Uncertainty Calibration for Active Domain Adaptation"☆35Updated 2 years ago
- Official PyTorch implementation of "Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error"☆34Updated last year
- Active Learning on a Budget - Opposite Strategies Suit High and Low Budgets☆92Updated 6 months ago
- [ICML'24] Conformal Prediction for Deep Classifier via Label Ranking☆12Updated 10 months ago
- Wasserstein Adversarial Active Learning☆29Updated 4 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆76Updated 2 years ago