StyleGAN: High-Quality Image Synthesis with GANs [Video]

This article was published as a part of the Data Science Blogathon.

Introduction to StyleGAN

Generative Adversarial Networks (GAN) are a somewhat new theory in Machine Learning, proposed for the first time in 2014. Their purpose is to synthesize artificial examples, such as pictures that are obscure from authentic photographs. A typical example of a GAN application is to produce artificial face pictures by learning from a dataset of notable faces.

• While GAN images became further vivid over time, one of their main hurdles is regulating their output, i.e. replacing explicit features such as pose, face shape, and hairstyle in an illustration of a face.
• The Style Generative Adversarial Network, or StyleGAN for short, is an addition to the GAN architecture that introduces significant modifications to the generator model. StyleGAN produces the simulated image sequentially, originating from a simple resolution and enlarging to a huge resolution (1024×1024).
• By transforming the input of each level individually, it examines the visual features that are …