A pytorch implementation of pix2pix + BEGAN (Boundary Equilibrium Generative Adversarial Networks)

Python code + notebooks to fully reproduce the results for the blog post "These Bored Apes Do Not Exist" on Medium. Blog post URL: https://medium.com/@nathancooperjones/these-bored-apes-do-not-exist-6bed2c73f02c

We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS.

Generate Faces using GANs (Part of Udacity's DLFND)

Generating Cat images using StyleGAN

Audio samples from "HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis"

Bidirectional Latent Optimized Generative Adversarial Networks

Taller de ML (Aprendizaje de Máquina) para crear imágenes artísticas (Generative Art) con redes Adversarias Generadoras y Condicionadas (GAN/CGAN) con los datos MINST de moda (Fashion MINST).

Generated faces from a pair of multi-layer neural networks generator and discriminator that compete against each other until one learns to generate realistic images of faces using CelebFaces Attributes (CelebA) dataset.

Pix2Pix Implementation for Facade Dataset

Utility files for Granular challenge.

Built a real-time website for image generation using gan-cls algorithm. The algorithm is trained on CUBS 200 birds dataset.

This repository houses projects completed as part of the Google Career Certificates program offered through the Orange Learning initiative, sponsored by Orange Digital Center, Google, and Coursera.

Use of Generative Adversarial Networks (GAN's) to generate new images of faces.

This is Udacity - Deep Learning Project 4 - Generate Faces

Udacity Deep Learning Nanodegree and all its projects.

Project Website for GP-GAN: Towards Realistic High-Resolution Image Blending

An unofficial PyTorch implementation of VQGAN

Face generation project for the DLND, Project 5