A Generative Adversarial Network (GAN) is a type of artificial intelligence model designed for generating new, realistic data samples. GANs were introduced by Ian Goodfellow and his colleagues in 2014. The unique aspect of GANs is that they consist of two neural networks, a generator, and a discriminator, which are trained simultaneously through adversarial training.

Here's an insight into how GANs work:

1. Generator:

   • The generator's role is to create synthetic data samples. It takes random noise as input and generates data that ideally resembles real samples from the training dataset.

   
   

2. Discriminator:

   • The discriminator's task is to distinguish between real and generated data. It is trained on both real and synthetic samples and aims to correctly classify them.

   
   

3. Adversarial Training:

   • The generator and discriminator are trained simultaneously through a competitive process. The generator tries to create increasingly realistic data to fool the discriminator, while the discriminator strives to become better at distinguishing real from fake data.

   
   
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