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Generative Adversarial Networks (GANs)

Towards Data Science

Generative Adversarial Networks (a.k.a. GANs) represents one of the most exciting recent innovation in deep learning. GANs were originally introduced by Ian Goodfellow and Yoshua Bengio from the…

Understanding Generative Adversarial Networks (GANs)

Towards Data Science

Generative Adversarial Networks (GANs) are deep generative models composed of two networks, a generator and a discriminator, opposed to each other.

Applications of Generative Adversarial Networks (GANs)

Towards AI

Introduction Today, computer science is regarded as one of the most influential fields in the world due to the emergence of deep learning. Hundreds of studies have been conducted to develop and optim...

Generative Adversarial Network

Level Up Coding

Generative Adversarial Networks or GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio’s lab. Since then, GANs have exploded in popularity. Here are a few examples to…

A Gentle Introduction to Generative Adversarial Networks (GANs)

Machine Learning Mastery

Last Updated on July 19, 2019 Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. Generative ...

A Friendly Introduction to Generative Adversarial Networks (GANs)

Python in Plain English

Let’s dive into the fascinating world of machine learning where creativity meets realism — the world of Generative Adversarial Networks (GANs). Imagine a tool that can generate hyper-realistic images,...

Generative Adversarial Networks

Towards Data Science

Designed by Ian Goodfellow and his colleagues in 2014, GANs consist of two neural networks that are trained together in a zero-sum game where one player’s loss is the gain of another.

Generative Adversarial Networks using Tensorflow

Towards Data Science

Generative adversarial networks (GANs) are deep neural net architectures comprising of a set of two networks which compete against the other, hence the name “adversarial”. GANs were introduced in a…

5 Articles to Understand Generative Adversarial Networks

Towards Data Science

A Generative Adversarial Network (GAN) is a generative network architecture, capable of generating new content, such as images and audio, made to look real. They can be used to generate special…

Exploring Generative Adversarial Networks (GANs) in Two-Dimensional Space

Python in Plain English

An implementation of a simple GAN. Photo by Kier in Sight on Unsplash Have you ever heard of a GAN? Well, the neural network architecture, which stands for Generative Adversarial Network, is essentia...

Generative Adversarial Networks in Python

Towards Data Science

Generative adversarial networks (GANs) are a set of deep neural network models used to produce synthetic data. The method was developed by Ian Goodfellow in 2014 and is outlined in the paper…

The math behind GANs (Generative Adversarial Networks)

Towards Data Science

The Generative Adversarial Network (GAN) comprises of two models: a generative model G and a discriminative model D. The generative model can be considered as a counterfeiter who is trying to…