Generative adversarial networks

GANs are deep neural net architectures that consist of two networks pitted against each other (hence the name "adversarial"). Ian Goodfellow et al. introduced GANs in a paper (see more at In GANs, the two main components are the generator and discriminator.

Working principle of Generative Adversarial Networks (GANs)

The Generator will try to generate data samples out of a specific probability distribution, which is very similar to the actual object. The discriminator will judge whether its input is coming from the original training set or from the generator part.

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