Generative Adversarial Network GAN - Learn Data Science with Travis

Travis chat

Filters

Generative Adversarial Network GAN

Generative Adversarial Networks (GANs) are a class of algorithms in deep learning that are designed for generating new instances of data by learning from real examples. They consist of two main components: a generator and a discriminator. The generator creates new data instances, while the discriminator evaluates them against real data, determining whether they are genuine or fake ².

The concept of GANs was introduced by Ian Goodfellow and his colleagues in 2014. Since then, they have gained significant popularity in various applications, including image generation, video generation, and even text generation ³. GANs operate through a process of adversarial training, where the generator and discriminator are trained simultaneously, leading to improved performance over time.

To build a GAN, one typically starts by loading a dataset, such as the MNIST dataset for handwritten digits. The model is then trained using hyperparameters like the number of epochs and learning rate, allowing the generator to produce images that closely resemble the real data ¹⁴.

If you’re interested in exploring GANs further, there are many resources available, including tutorials and notebooks that can help you understand their implementation better.

Generative Adversarial Network(GAN)

Analytics Vidhya

understand by creating a model which generates images of handwritten digits similar to those from the MNIST database. Generative modeling is an unsupervised learning task in machine learning that…

Generative Adversarial Network

Towards Data Science

Generative Adversarial Networks are used for generating new instances of data by learning from real examples. It has two main components a generator and a discriminator.

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…

Generative Adversarial Networks | GANs

Towards Data Science

GANs are a relatively recent invention in the field of ML. It was introduced by Ian Goodfellow et al. in 2014 through this amazing research paper. Let us first look at the terms individually —…

Generative Adversarial Networks

Analytics Vidhya

Generative Adversarial Networks (GANs) are a class of algorithms used in Deep Learning which belong to the category of generative models. With “generative models” we refer to those models whose main…

Generative Adversarial Networks GANs: A Beginner’s Guide

Towards Data Science

The hypothetical example of Machine Learning is imagined around having a machine that is able to think and mimic passing a test with some degree of intelligent. Although this the ultimate goal, we…

GANs — Generative Adversarial Networks

Towards Data Science

Generative Adversarial Networks A dive into the magical world of deep learning, unlocking the artistic capabilities of your machine.

Introduction to GANs

Analytics Vidhya

Generative Adversarial Networks also commonly referred to as GANs are used to generate images without very little or no input. GANs allow us to generate images created by our Neural…

An Easy Introduction to GANs

Level Up Coding

A GAN or a Generative Adversarial Network is a neural network architecture that was first proposed by Ian Goodfellow et.al. in 2014. The idea was to train neural networks to learn the probability…

Learning Generative Adversarial Networks (GANs)

Analytics Vidhya

GANs were introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal in 2014. A generative adversarial network (GAN) is a type of model in a neural network that…

Generative Adversarial Learning

Towards Data Science

This post explains concepts behind generative adversarial networks and gives some insight into via GANs work better than generative models for generating realistic fakes.

Deep Convolutional Generative Adversarial Networks

Dive intro Deep Learning Book

In Section 20.1 , we introduced the basic ideas behind how GANs work. We showed that they can draw samples from some simple, easy-to-sample distribution, like a uniform or normal distribution, and tra...