This project comes from a Kaggle Competiton named Generative-Dog-Images.
Data is based on the Stanford Dogs Dataset.
Authors: Gaofeng Huang, Jun Ying, Xi Zhang. Final project of the course Machine Learning II (by Prof. Amir Jafari).
This project applied a technique called Generative Adversarial Network (GAN).
GAN is a the-state-of-art deep learning algorithm proposed by Ian Goodfellow in 2014.
GAN is a combination of generator network and discriminator network.
These two networks compete with each other and form a zero-sum game.
A zero-sum game means if one gets better, another will must be worse.
As two networks optimize their perfomance when their opponents get better.
There is a Nash Equilibrium when both generator and discriminator reach to their best performance.
This project is going to create a model, which can generate new dog images beyond the training dataset.