GAN

 

Generative adversarial network (GAN) is a machine learning model used in unsupervised learning where two neural networks compete against each other.  Both networks are trained simultaneously through adversarial training to become more accurate in their predictions.

The architecture of GAN would have one generator network that artificially creates random outputs that look real and one discriminator network that identifies whether the outputs are real or not.  As the training progresses, the generator learns to produce samples that are increasingly difficult for the discriminator to distinguish from the real ones.  At convergence, the generator generates samples that are almost indistinguishable from real data.

GAN model was first written in a research paper ‘Generative Adversarial Nets’ by Ian Goodfellow et al. from the University of Montreal in 2014.  Since then, GAN models have seen their growth in image, video, and text generation because GANs are more focused on generating new samples where previous samples have not existed before.  The major benefit of GANs is that they can be used to create new data outputs where data collection is difficult or impossible. 

Some examples of real-world GAN models include (1) NVIDIA's GANverse3D that generates 3D models from single 2D images, (2) The Fabricant, the digital fashion house that generates digital innovative clothing designs, or (3) This-person-does-not-exist, a website that generates lifelike images of human faces that don't belong to real people.  

 

In the next post, we will explore a different model that also excels at image, video, and text generation.