Generative adversarial networks (GANs) are one of the hottest topics in deep learning. (GANs) are a class of artificial algorithms used in unsupervised learning algorithm, implemented by a system of two neural networks
Both networks are contesting with each other in the Zero-Sum Game Framework. Generative Adversarial Networks(GANs) are a set of models that basically learn to create synthetic data that is similar to input data it’s given.
The discriminator has the task of determining whether a given image looks natural (i.e, is an image from the dataset) or looks like it has been artificially created. The task of the generator is to create natural looking images that are similar to the original data distribution, images that look natural enough to fool the discriminator network. Firstly a random noise is given to the Generator, using this it creates the Fake images and then these Fake images are along with original images sent to the Discriminator.
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