wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

InfoGAN implemented with tensorflow

The following study, through which we can generate X-ray images of the chest region in a semi-conditional manner, by taking advantage of the probability distributions.

Implementation and Experimentation of InfoGANs

Implementing the InfoGAN architecture in Keras for learning about disentangled representation of salient features of input images in a completely unsupervised manner.

A novel approach, named SamplerGAN, for generating high-quality labeled data

The Generative Adversarial Networks with Python would serve as our primary reference throughout the project. The models would be trained on the MNIST dataset. The official TensorFlow framework and documentation will be used to implement the different architectures on Python. These papers would be used to implement various evaluation met

PyTorch implementation of InfoGAN

Implementations of Generative Adversarial Networks and AutoEncoders in Pytorch

objected oriented implementation of InfoGAN using PyTorch

some examples by pytorch

My TensorFlow/Keras implementation of InfoGAN

General Adversial Networks using Few shot learning

The basic tutorial of tensorflow

Implementation of InfoGAN using PyTorch lightning

Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

This is a deep learning code written in PyTorch that convert a given text into image.

GAN / DCGAN / InfoGAN / BEGAN ...