karamsahoo/abstractgan

Creating new abstract arts using a Generative Adversarial Network.

AbsractGAN

Creating new abstract art using a Generative Adversarial Network.

What are GANS?

Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks.

Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.

GANs are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples, and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples.

Why abstract art?

Abstract art is art that does not attempt to represent an accurate depiction of a visual reality but instead use shapes, colours, forms and gestural marks to achieve its effect. Abstract art uses visual language of shape, form, color and line to create a composition which may exist with a degree of independence from visual references in the world. Often there is no predetermined rules which exist in the more stricter genres of art such as perspective or color schemes. Using a GAN to generate new art sounds like a good plan because the innate randomness in colors and structres in the generated image is preserved due to the very nature of GAN. Also if one is looking for ideas and wants a totally out of the box ideas then GANs can help in switching that creative switch on.

Demo

This is what we started with:

Dataset

The dataset is available in Kaggle. Click here to get the dataset. The images are scrapped from the web hence require additinal resizing and reshaping before feeding to the GAN network.

Methodology

Results

Run Locally

Clone the project

  git clone https://link-to-project

Go to the project directory

  cd my-project

Install dependencies

  npm install

Start the server

  npm run start