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

Generative Adversarial Networks: An Overview

Academic paper

Chaoyue Wang, Chang Xu, Xin Yao, Dacheng Tao

Evolutionary Generative Adversarial Networks

Academic paper

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

Generative Adversarial Nets

Academic paper

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

Generative Adversarial Networks

Academic paper

Stephan Halbritter

Generative Adversarial Networks

Academic paper

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