Answer / Sumit Kumar Charak

Generative Adversarial Networks (GANs) are a type of machine learning model that consist of two components: a generator and a discriminator. The generator creates new samples, while the discriminator tries to classify these samples as real or fake. During training, both networks iteratively improve their performance, with the generator trying to produce more realistic samples and the discriminator becoming better at detecting fakes. Eventually, the generator can generate high-quality samples that are indistinguishable from real data.

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