Tag - Generative Adversarial Networks

CAN (Creative Adversarial Network) - Explained

GANs (Generative Adversarial Networks), a type of Deep Learning networks, have been very successful in creating non-procedural content. This work explores the possibility of machine generated creative content. By Harshvardhan Gupta, Hacker Noon. Late...

Example of things it can generate

How do GANs intuitively work? GANs or Generative Adversarial Networks are a kind of neural networks that is composed of 2 separate deep neural networks competing each other: the generator and the discriminator. Their goal is to generate data points t...

Generative Adversarial Networks, an overview

In this article, we’ll explain GANs by applying them to the task of generating images. One of the few successful techniques in unsupervised machine learning, and are quickly revolutionizing our ability to perform generative tasks. comments By Keshav...

Generative Adversarial Networks — Part II

Second part of this incredible overview of Generative Adversarial Networks, explaining the contributions of Deep Convolutional-GAN (DCGAN) paper. By Zak Jost, Amazon. comments In Part I of this series, the original GAN paper was presented. Although b...

The goal of Generative Models

Abusing Generative Adversarial Networks to Make 8-bit Pixel Art Generative models allow a computer to create data — like photos, movies or music — by itself. A little over a year ago, Alec Radford (building on the work of Ian Goodfellow) published a...

The New Neural Internet is Coming

The Generative Adversarial Networks (GANs) are the first step of neural networks technology learning creativity. comments By Oleksandr Savsunenko, LetsEnhance. How it all began / The Landscape Generative Adversarial Networks progress Think of the typ...