kumar-shridhar/PyTorch-BayesianCNN

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1570 stars · 335 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch library implementing Bayesian convolutional neural networks with variational inference via Bayes by Backprop. It provides drop-in Bayesian replacements (BBBConv2d, BBBLinear) for standard PyTorch layers, with optional local reparametrization trick.

Use cases

• estimate uncertainty in image classification predictions
• build a Bayesian CNN in PyTorch
• compare Bayesian vs frequentist CNN performance on MNIST or CIFAR
• apply Bayes by Backprop to convolutional layers
• quantify aleatoric uncertainty in deep learning models
• reproduce research on variational inference in CNNs

When to choose

• you need uncertainty estimates from a CNN in PyTorch
• you want drop-in Bayesian layers for an existing PyTorch model
• you are doing research or coursework on Bayesian deep learning

When to avoid

• you need production-grade, actively maintained deep learning tooling
• you need Bayesian layers beyond conv2d and linear
• you want the latest PyTorch features or GPU-optimized Bayesian inference

Facets

library · maturity maintenance

machine-learning deep-learning deep-learning computer-vision machine-learning python bayesian-deep-learning bayes-by-backprop variational-inference uncertainty-estimation pytorch convolutional-neural-networks research-code

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