Generating Anime Images by Implementing DC GAN paper
AnimeGAN
PyTorch implementation of DCGAN introduced in the paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford, Luke Metz, Soumith Chintala.
Generative Adversarial Networks (GANs) are one of the most popular (and coolest)
Machine Learning algorithms developed in recent times. They belong to a set of algorithms called generative models, which
are widely used for unupervised learning tasks which aim to learn the uderlying structure of the given data. As the name
suggests GANs allow you to generate new unseen data that mimic the actual given real data. However, GANs pose problems in
training and require carefullly tuned hyperparameters.This paper aims to solve this problem.
DCGAN is one of the most popular and succesful network design for GAN. It mainly composes of convolution layers
without