>*“Every artist dips his brush in his own soul, and paints his own nature into his pictures.”*
-Henry Ward Beecher
 
We recognize the works of artists through their unique style, such as color choices or brush strokes. The “je ne sais quoi” of artists like Claude Monet can now be imitated with algorithms thanks to generative adversarial networks (GANs). In this getting started competition, you will bring that style to your photos or recreate the style from scratch!
 
Computer vision has advanced tremendously in recent years and GANs are now capable of mimicking objects in a very convincing way.  But creating museum-worthy masterpieces is thought of to be, well, more art than science. So can (data) science, in the form of GANs, trick classifiers into believing you’ve created a true Monet? That’s the challenge you’ll take on!


## The Challenge:
 
A GAN consists of at least two neural networks: a generator model and a discriminator model. The generator is a neural network that creates the images. For our competition, you should generate images in the style of Monet. This generator is trained using a discriminator.
 
The two models will work against each other, with the generator trying to trick the discriminator, and the discriminator trying to accurately classify the real vs. generated images.
 
Your task is to build a GAN that generates 7,000 to 10,000 Monet-style images. 


## Getting Started:
 
Details on the dataset can be found [here](https://www.kaggle.com/c/gan-getting-started/data) and an overview of the evaluation process can be found [here](https://www.kaggle.com/c/gan-getting-started/overview/evaluation).
 
To learn how to submit and answers to other FAQs, review the [Frequently Asked Questions](https://www.kaggle.com/c/gan-getting-started/overview/frequently-asked-questions).
Recommended Tutorial
We highly recommend
[Amy Jang's notebook](https://www.kaggle.com/amyjang/monet-cyclegan-tutorial)
that goes over the basics of loading data from TFRecords, using TPUs, and building a CycleGAN.
Although the competition dataset only includes Monet images, check out this [dataset](https://github.com/junyanz/CycleGAN) for Cezanne, Ukiyo-e, and Van Gogh paintings to run your GAN on.