Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Easy-to-follow Pytorch tutorial Notebook for Multi-Agent-Diverse-Generative-Adversarial-Networks
Collection of tensorflow notebooks tutorials for implementing some basic Deep Learning architectures.
An IPython notebook explaining the concepts of Variational Autoencoders and building one using Keras to generate new faces.
This project implements a Generative Adversarial Network (GAN) to generate fashion images using the Fashion MNIST dataset. The notebook contains a complete implementation of both generator and discriminator models, training loop, and visualization of generated images.
Released June 10, 2014