Generative Adversarial Networks: An Overview.

IEEE Signal Processing Magazine(2018)

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摘要
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, i...
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关键词
Machine learning,Generators,Training data,Data models,Convolutional codes,Image resolution,Signal resolution,Semantics
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