GANs — Generative Adversarial Networks 101

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN, and LSGAN models with MNIST and CIFAR-10 datasets.

Generative Adversarial Networks by fernanda rodríguez

In this series, an introduction to the basic notions that involve the concept of Generative Adversarial Networks will be presented.

“…the most interesting idea in the last 10 years in ML”. Yann LeCun

Next, a complete list of our articles covers the definition and some of the leading models of GANs. The models include a brief theoretical introduction and practical implementations developed using Python and Keras/TensorFlow in Jupyter.

Introduction

GANs — A brief overview (Part 1)

Models

The definition and training of some models with MNIST and CIFAR10 datasets are presented.

MNIST Models

GANs — Generative Adversarial Nets MNIST (Part 2)

GANs — Deep Convolutional GANs with MNIST (Part 3)

GANs — Conditional GANs with MNIST (Part 4)

GANs — Context-Conditional GANs with MNIST (Part 5)

GANs — Wasserstein GAN with MNIST (Part 6)

GANs — Least Squares GANs with MNIST (Part 7)

CIFAR10 Models

GANs — Deep Convolutional GANs with CIFAR10 (Part 8)

GANs — Conditional GANs with CIFAR10 (Part 9)

Github repository

Look the complete training models using Python and Keras/TensorFlow in Jupyter Notebook.

mafda/generative_adversarial_networks_101

References

Related papers

• Generative Adversarial Networks
• Unsupervised Representation Learning With Deep Convolutional
• Conditional Generative Adversarial Nets
• Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
• Wasserstein GAN
• Least Squares General Adversarial Networks

Datasets

• THE MNIST DATABASE of handwritten digits
• The CIFAR-10 dataset

Other repositories

• Keras-GAN
• Adversarial Nets Papers
• How to Train a GAN? Tips and tricks to make GANs work
• The GAN Zoo
• GAN Lab: An Interactive, Visual Experimentation Tool for Generative Adversarial Networks
• gans-awesome-applications
• tensorflow-generative-model-collections

Thanks for reading, stay awesome! ❤