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What are Generative Adversarial Networks ?
Generative Adversarial Networks (GANs) are a type of neural network that is unsupervised learning. GANs are essentially a system of two competing neural network models that compete for the ability to assess, capture, and copy variations within a dataset.
How does GANs work?
There are three task in Generative Adversarial Networks algorithm :
Generative
Adversarial
Networks
Generative : It describes how data is generated in terms of a probabilistic model.
Adversarial : Training model done in Adversarial
Networks : uses deep learning neural networks for the training purpose.
This technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics.
The idea of generative Adversarial Networks GAN is based on the "indirect" training through the discriminator; another neural network tells you how much an input is "realistic", which itself is also being updated dynamically.
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