/synthetic-data-gan

Experimenting with generating synthetic data using ydata-synthetic

Primary LanguageJupyter NotebookMIT LicenseMIT

Artificial Data Generation using GANs

1. GAN - Generative Adversarial Networks

An excellant and more detailed read on GANs: Google Developers

1.1 GAN Structure

A GAN consists of 2 parts - a generator and a discriminator, both neural networks which try to compete with each other.

  • The Generator tries to generate content which is ideally supposed to look like the real content, which can be image, text, a just numerical data in general.

    • The generator is penalised if the discriminator is able to distinguish between real and generated content

  • The discriminator tries to tell apart the generated content and the real content

    • The discriminator is penalised if it's not able to distinguish between real and generated content

1.2 GAN Training

  • Discriminator
    • Tries to classify real data from generated one
    • Discriminator loss is calculated
    • Gradients of discriminator are computed through backpropagation based on discriminator's loss
    • Computed gradients used to change discriminator's weights

  • Generator
    • Produce output based on random input
    • Discriminator tries to classify this output as real or fake
    • Generator loss is calculated based on discriminator's classification efforts
    • Gradients of generator are computed through backpropagation based on generator's loss
    • Computed gradients used to change generator's weights