TorchUncertainty

Practitioner-friendly Uncertainty

Getting Started API Documentation
  • Easy-to-use PyTorch uncertainty quantification tools
  • Seamless integration with Lightning
  • Classification, regression, and segmentation tasks
  • Open-source
Classification

Identifying categories while quantifying uncertainty.

Applications: Risk-sensitive predictions, anomaly detection...

Algorithms: Deep Ensembles, Packed Ensembles, Bayesian Neural Networks

Classification
Regression

Predicting continuous-valued outputs with uncertainty bounds.

Applications: Forecasting, scientific analysis...

Algorithms: Deep Evidential Regression

Regression
Segmentation

Pixel-wise predictions with uncertainty metrics.

Applications: Image segmentation.

Algorithms: Deep Ensembles, Packed Ensembles,

Segmentation
Post-hoc Methods

Improving model predictions with post-hoc methods.

Applications: Risk management, decision-making systems...

Algorithms: Temperature Scaling, Conformal RAPS

Post-hoc Methods
Bayesian Methods

Bayesian-inspired approaches to estimate model uncertainty by treating parameters or predictions as probabilistic distributions.

Applications: Uncertainty quantification, decision-making under uncertainty, probabilistic predictions.

Algorithms: Monte Carlo Dropout, Variational Inference, MCBN

Bayesian Methods
Ensemble Methods

Combining predictions from multiple models to improve accuracy and provide reliable uncertainty estimates.

Applications: Robust predictions, anomaly detection, improved generalization.

Algorithms: Deep Ensembles, Packed Ensembles

Ensemble Methods