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Generative adversarial network

Generative adversarial network

Deep learning method that trains two networks simultaneously without extensively annotated training data that compete with each other in a zero-sum game framework.

Generative Adversarial Network (GAN) is a deep learning method that simultaneously trains two networks. A generator that learns to generate fake samples from an unknown distribution or noise and a discriminator that learns to identify fake from real samples.

It aims to model the natural image distribution by forcing the generated samples to be indistinguishable

from natural images. GANs enable a various applications such as image generation, representation

learning, image manipulation, object detection and video applications.

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Evolutionary Generative Adversarial Networks

Chaoyue Wang, Chang Xu, Xin Yao, Dacheng Tao

Academic paper

GANs in Action

Jakub Langr and Vladimir Bok

Web

Generative Adversarial Nets

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,Sherjil Ozair, Aaron Courville and Yoshua Bengio

Academic paper

Generative Adversarial Networks

Moacir A. Ponti, Leonardo S. F. Ribeiro, Tiago S. Nazare, Tu Bui and John Collomosse

Academic paper

Generative Adversarial Networks

Stephan Halbritter

Academic paper

Generative Adversarial Networks: An Overview

Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A Bharath

Academic paper

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