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.
Sketch2Color-anime-translation
Given a simple anime line-art sketch the model outputs a decent colored anime image using Conditional-Generative Adversarial Networks (C-GANs) concept.
arbitrary-text-to-image-papers
A collection of arbitrary text to image papers with code (constantly updating)
Pytorch-conditional-GANs
Implementation of Conditional Generative Adversarial Networks in PyTorch
deep-learning-roadmap
my own deep learning mastery roadmap
ContraD
Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)
CPPN-WGAN
Generative Art Experiments
BicycleGAN
Toward Multimodal Image-to-Image Translation
CycleGAN
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
iGAN
Interactive Image Generation via Generative Adversarial Networks
pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch