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Keras-GAN is a collection of generative adversarial network implementations built with Keras for synthetic data generation and image manipulation. It provides frameworks for image-to-image translation, image inpainting, and neural image super-resolution. The library includes tools for learning disentangled latent space representations to control specific attributes of synthetic outputs. It also features capabilities for image domain translation using paired or unpaired data and the ability to fill corrupted or missing image parts by analyzing surrounding visual context. The project covers ge
PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative adversarial network architectures. It serves as a toolkit for training and evaluating models that utilize adversarial minimax optimization to produce synthetic data, offering a structured environment for exploring complex generative tasks within the PyTorch ecosystem. The library distinguishes itself through a comprehensive suite of image synthesis and manipulation capabilities, including super-resolution, inpainting, and cross-domain style translation. It supports advanced training m
This is a library of generative model architectures built using the TensorFlow framework. It provides implementations for producing synthetic data and realistic images, specifically focusing on Variational Autoencoders and various Generative Adversarial Network variants. The collection includes specific GAN architectures such as WGAN-GP, LSGAN, InfoGAN, and EBGAN. It also features Variational Autoencoders designed to learn latent representations and synthesize new samples from learned distributions. The project covers image processing pipelines for normalizing and cropping data, as well as a
ARAE-Tensorflow for Discrete Sequences (Adversarially Regularized Autoencoder)
Generated using only 100 images of Obama, grumpy cats, pandas, the Bridge of Sighs, the Medici Fountain, the Temple of Heaven, without pre-training.
The main features of justinpinkney/data-efficient-gans are: Generative Model Implementations.
Open-source alternatives to justinpinkney/data-efficient-gans include: aboev/arae-tf — ARAE-Tensorflow for Discrete Sequences (Adversarially Regularized Autoencoder). eriklindernoren/keras-gan — Keras-GAN is a collection of generative adversarial network implementations built with Keras for synthetic data… eriklindernoren/pytorch-gan — PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative… hwalsuklee/tensorflow-generative-model-collections — This is a library of generative model architectures built using the TensorFlow framework. It provides implementations… justinpinkney/stylegan2 — Various Improvements to make StyleGAN2 more suitible to be trained on Google Colab Supports Non-Square images, for… mit-han-lab/data-efficient-gans — Generated using only 100 images of Obama, grumpy cats, pandas, the Bridge of Sighs, the Medici Fountain, the Temple of…