lweitkamp/GANs-JAX
Implementation of several Generative Adversarial Networks in JAX / Flax
This project provides pre-built examples of Generative Adversarial Networks (GANs) using the JAX and Flax frameworks. It helps machine learning engineers and researchers explore and implement advanced GAN architectures for tasks like synthetic image generation, taking in configurations and outputting trained models. The primary users are deep learning practitioners working with generative models.
No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher looking for readily available, performant implementations of various GAN models to generate synthetic images or understand their training dynamics.
Not ideal if you are an end-user without deep learning experience, as this project requires familiarity with JAX, Flax, and GAN concepts.
Stars
35
Forks
5
Language
Jupyter Notebook
License
—
Category
Last pushed
Apr 29, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/lweitkamp/GANs-JAX"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
explosion/thinc
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
google-deepmind/optax
Optax is a gradient processing and optimization library for JAX.
patrick-kidger/diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable....
google/grain
Library for reading and processing ML training data.
patrick-kidger/equinox
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/