shayneobrien/generative-models
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
This project offers clear, visually-backed examples of various generative models like GANs and VAEs. It helps machine learning engineers and researchers understand how these models are implemented in PyTorch. You input a specific generative model type, and the output is a working, annotated implementation that can generate new, similar data from an existing dataset.
504 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher looking for understandable PyTorch implementations of generative models for learning or as a starting point for your own projects.
Not ideal if you need a production-ready solution for generating complex images or data, as the default architectures are simple for clarity.
Stars
504
Forks
72
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 19, 2018
Commits (30d)
0
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