GAN implementations / deep learning
Keras implementations of a wide variety of GAN architectures for educational/reference use.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Modern PyTorch GAN library with many models; choose if you prefer PyTorch over Keras.
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
Book companion with Keras GAN notebooks; choose for guided, tutorial-style material.
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
Broader generative model collection in TensorFlow/PyTorch; choose for multi-framework coverage.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Legacy Keras GAN library; choose for old Keras 1.x projects but it is stale.
Keras Generative Adversarial Networks
A single simple GAN in PyTorch; choose only for a minimal PyTorch baseline.
A very simple generative adversarial network (GAN) in PyTorch
A single DCGAN example in Keras; choose for a narrow, outdated DCGAN reference.
Keras implementation of Deep Convolutional Generative Adversarial Networks
These projects were analysed and named Keras-GAN among their alternatives. The relationship is not symmetric — how Keras-GAN rates them is a separate judgement, made when Keras-GAN is analysed in its own right.
calls Keras-GAN “choose it to stay in Keras rather than PyTorch for GAN implementations.”
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
calls Keras-GAN “choose when you prefer Keras/TensorFlow high-level GAN examples over PyTorch/TensorFlow low-level scripts.”
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
calls Keras-GAN “Choose for a dedicated Keras GAN example collection if your focus is generative models, not general TensorFlow.”
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
calls Keras-GAN “Narrower: only GANs, and uses Keras rather than NumPy from scratch.”
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
calls Keras-GAN “A collection of Keras GAN implementations including CycleGAN, but old and broader in scope.”
Image-to-Image Translation in PyTorch
calls Keras-GAN “GAN-only implementations in Keras; choose if you specifically want Keras GAN examples despite unmaintained status.”
Machine learning, in numpy
calls Keras-GAN “choose for a collection of standalone Keras GAN implementations to study GAN variants; stale and narrow.”
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
calls Keras-GAN “When you want a collection of Keras GAN implementations, but it's old (3.7 years) and specific to GANs.”
A collection of machine learning examples and tutorials.
calls Keras-GAN “Choose it if you only need Keras GAN examples and not a full deep learning tutorial.”
Build your neural network easy and fast, 莫烦Python中文教学