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Generative Adversarial Network

Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.

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We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS.

  • Updated May 7, 2018
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This repository contains code and bonus content which will be added from time to time for the books "Learning Generative Adversarial Network- GAN" and "R Data Analysis Cookbook - 2nd Edition" by Packt

  • Updated Dec 27, 2021
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Released June 10, 2014

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deep-learning neural-network