Ctrl+K

Lightning-UQ-Box

Lightning-UQ-Box#

Welcome to the lightning-uq-box documentation page. Our goal is to give you both an intuition of how different UQ-Methods work as well as demonstrate how you can apply these methods in your research or projects. To this end, we aim to give both a theoretical and practical overview of implemented UQ-Methods since there exist a wide variety of UQ-Methods. Similarly, there are several general tasks for which practitioners might require uncertainty estimates. The library currently supports the following four tasks:

  1. Regression for tabular/image inputs with 1D scalar targets

  2. 2D Regression / Pixel Wise Regression

  3. Classification for tabular/image inputs with single classification label

  4. Segmentation where each pixel is assigned a class

While some UQ-Methods like MC-Dropout or Deep Ensembles can be applied across tasks, other methods are specifically developed for certain tasks. The following aims to give an overview of supported methods for the different tasks.

In the tables that follow below, you can see what UQ-Method/Task combination is currently supported by the Lightning-UQ-Box via these indicators:

  • ✅ supported

  • ❌ not designed for this task

  • ⏳ in progress

To get started,

$ pip install lightning-uq-box

Classification of UQ-Methods#

The following sections aims to give an overview of different UQ-Methods by grouping them according to some commonalities. We agree that there could be other groupings as well and welcome suggestions to improve this overview. We also follow this grouping for the API documentation in the hopes to make navigation easier.

Single Forward Pass Methods#

Single Forward Pass Methods#

UQ-Method

Regression

Classification

Segmentation

Pixel Wise Regression

Quantile Regression (QR)

✅

❌

❌

✅

Deep Evidential (DE)

✅

⏳

⏳

✅

Mean Variance Estimation (MVE)

✅

❌

❌

✅

Approximate Bayesian Methods#

UQ-Method

Regression

Classification

Segmentation

Pixel Wise Regression

Bayesian Neural Network VI ELBO (BNN_VI_ELBO)

✅

✅

✅

⏳

Bayesian Neural Network VI (BNN_VI)

✅

⏳

⏳

⏳

Deep Kernel Learning (DKL)

✅

✅

❌

❌

Deterministic Uncertainty Estimation (DUE)

✅

✅

❌

❌

Laplace Approximation (Laplace)

✅

✅

❌

❌

Monte Carlo Dropout (MC-Dropout)

✅

✅

✅

✅

Stochastic Gradient Langevin Dynamics (SGLD)

✅

✅

⏳

⏳

Spectral Normalized Gaussian Process (SNGP)

✅

✅

❌

❌

Stochastic Weight Averaging Gaussian (SWAG)

✅

✅

✅

✅

Deep Ensemble

✅

✅

✅

✅

Generative Models#

UQ-Method

Regression

Classification

Segmentation

Pixel Wise Regression

Classification And Regression Diffusion (CARD)

✅

✅

❌

❌

Probabilistic UNet

❌

❌

✅

❌

Hierarchical Probabilistic UNet

❌

❌

✅

❌

Post-Hoc methods#

UQ-Method

Regression

Classification

Segmentation

Pixel Wise Regression

Test Time Augmentation (TTA)

✅

✅

⏳

⏳

Temperature Scaling

❌

✅

⏳

❌

Conformal Quantile Regression (Conformal QR)

✅

❌

❌

⏳

Regularized Adaptive Prediction Sets (RAPS)

❌

✅

❌

❌

Image to Image Conformal

❌

❌

❌

✅

Table of contents#

Contents