/imgAN

Training and testing Generative Models (GANs) on image datasets

Primary LanguageJupyter NotebookMIT LicenseMIT

imgAN

GitHub GitHub top language GitHub last commit

A Project on Training, Testing, Classification and Detection of images generated via GANs

by Yash Bhardwaj

Generative Modelling

Deep neural networks are used mainly for supervised learning: classification or regression. Generative Adversarial Networks or GANs, however, use neural networks for a very different purpose: Generative modeling

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. - Source

To get a sense of the power of Generative models, visit thispersondoesnotexist.com. Every time this page is refreshed, a new image of a non-existent person's face is generated in real time.