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

Generating Cat images using StyleGAN

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

💤🖌️ AI making tiny Bitsy video games. Features an experimental generative structure inspired by GANs and Genetic Algorithms

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

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.

An unofficial PyTorch implementation of VQGAN

A deep neural network for kernel-blind image deblurring

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

Cardiac Fats Segmentation Using a Conditional Generative Adversarial Network

Pix2Pix Implementation for Facade Dataset

Udacity Deep Learning Nanodegree and all its projects.

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

Using DCGAN architecture to generate faces from CelebA dataset containing faces of some celebrities, made in PyTorch. Do 🌟 the repo if you find it useful.

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

A Deep Learning project to Generate faces using GANs

Using generative adversarial networks to generate new images of faces (datasets: MNIST, CelebA).

Working with GANs and PINNs for Deep Learning for Science Course

Defined and trained a DCGAN on a dataset of faces. The Goal of this project is to generate new images of faces that look as realistic as possible.

Implementation of Conditional Deep Convolutional GANs in low-level APIs