tjwei/GANotebooks resource

Health v2 · maintenance only

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1484 stars · 362 forks observed · 2026-08-28

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

A collection of Jupyter notebook implementations of generative adversarial networks, including WGAN, improved WGAN-GP, InfoGAN, DCGAN, CycleGAN, pix2pix, and DRAGAN, written in Lasagne, Keras, and PyTorch. It serves as educational reference code with sample results for image-to-image translation and image generation tasks.

Use cases

• learn how GANs are implemented from scratch
• study WGAN and WGAN-GP implementations
• understand InfoGAN on MNIST
• implement pix2pix image-to-image translation
• run CycleGAN for unpaired image translation
• compare GAN implementations across Keras, Lasagne, and PyTorch
• generate anime faces with a GAN

When to choose

• you want readable notebook-style reference implementations of classic GAN variants
• you are learning generative adversarial networks and want to see multiple frameworks side by side
• you need example code for WGAN, InfoGAN, CycleGAN, or pix2pix

When to avoid

• you need a maintained production-ready GAN library
• you want modern architectures like diffusion models or StyleGAN
• you require support for recent versions of TensorFlow, Keras, or PyTorch
• you need documentation beyond the notebooks themselves

Facets

learning-resource · maturity abandoned

machine-learning deep-learning image-processing deep-learning machine-learning image-processing python gan wgan wgan-gp infogan dcgan cyclegan pix2pix dragan keras lasagne pytorch jupyter-notebooks generative-models gpu

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