Generative Adversarial Network topic
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.
DeepNetworks
My implementations of deep neural networks for practice.
the-gan-zoo
A list of all named GANs!
Improved-Wasserstein-GAN-application-on-MRI-images
Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images
video_prediction
Stochastic Adversarial Video Prediction
CS231n
PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"
StyleGAN-pytorch
PyTorch implementation of A Style-Based Generator Architecture for Generative Adversarial Network
ChemGAN-challenge
Code for the paper: ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv preprint arXiv:1708.08227.
DeepNAG
Deep Non-Adversarial Gesture Generation
GANimation
GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]
Deep-learning-with-Python
Deep learning codes and projects using Python