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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Further Resources
Evolutionary Generative Adversarial Networks
Chaoyue Wang, Chang Xu, Xin Yao, Dacheng Tao
Academic paper
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