andrewgordonwilson/bayesgan

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1013 stars · 172 forks observed · 2026-08-28

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

TensorFlow implementation of the Bayesian GAN from the NIPS 2017 paper by Saatchi and Wilson, which places conditional posteriors over generator and discriminator weights and marginalizes them via stochastic gradient Hamiltonian Monte Carlo. It includes scripts for synthetic data and standard image datasets like MNIST, CIFAR10, CelebA, and SVHN.

Use cases

• reproduce the Bayesian GAN paper results
• train a GAN that avoids mode collapse
• semi-supervised learning with generative adversarial networks
• sample from a probabilistic ensemble of generative models
• run GAN experiments on MNIST or CelebA
• research Bayesian approaches to deep generative models

When to choose

• you want the reference implementation of the Bayesian GAN for research or paper reproduction
• you need a GAN formulation with uncertainty over weights and resistance to mode collapse
• you are studying Bayesian deep learning methods

When to avoid

• you need modern, maintained code - it requires Python 2.7 and TensorFlow 1.0.0
• you want production-ready GAN training with current frameworks
• you need a general-purpose GAN library rather than a single paper's code

Facets

library · maturity abandoned

machine-learning deep-learning data-generation machine-learning deep-learning artificial-intelligence python gan bayesian-deep-learning tensorflow research-code nips-2017 linux

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