Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

Natural Gradient Boosting for Probabilistic Prediction

A Library for Uncertainty Quantification.

An extension of XGBoost to probabilistic modelling

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.

An extension of LightGBM to probabilistic modelling

CVPR 2020 - On the uncertainty of self-supervised monocular depth estimation

[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

Quantile Regression Forests compatible with scikit-learn.

[CVPR 2022 Oral] Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

[CVPR 2024 Award Candidate] Producing and Leveraging Online Map Uncertainty in Trajectory Prediction

GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation [IJCV2023]

[CVPR 2024 Oral, Best Paper Award Candidate] Official repository of "PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness"

Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.

[ICCV'23] Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks

Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.

Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).

Official Implementation of "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions" (ICLR, 2022)

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.