Deep Learning – Introduction to Generative Adversarial Neural Networks (GANs)

Generative Adversarial Networks (GANs) – A very young family member of Deep Neural Networks Architecture. Introduce by Ian Goodfellow and his team at the University of Montreal in 2014. GANs are class of unsupervised machine learning algorithm.

 Adversarial training “The most interesting idea in the last 10 years in the field of Machine Learning.”                      – Sir. Yann LeCun, Facebook’s AI research director.

Lets Unpack This Jargon

As per the definition of word “Adversarial” from open internet “Involving two people or two sides who oppose each other i.e. adversary procedures. An adversarial relationship an adversarial system of justice with prosecution and defence opposing each other.

GAN Titel.png


So as name suggest it is called as Adversarial Networks because this is made up of two neural networks. Both neural networks are assigned different job role i.e. contesting with each other.

  • Neural Network one is called as Generator, because it generate new data instances.
  • Other neural net is called as Discriminator, evaluates work for first neural net for authenticity.

The cycle continue to obtain accuracy or near perfection results. Still confused, its ok let me try to give real world example as below.