Generative Adversarial Networks (GANs) are a type of deep learning model that consists of two neural networks: a generator and a discriminator. GANs were first introduced by Ian Goodfellow and his colleagues in 2014. The main idea behind GANs is to train the generator network to generate realistic data samples that are similar to the training data, while the discriminator network tries to distinguish between real and fake samples. The two networks are trained simultaneously in a competitive manner, hence the term "adversarial". Here's how GANs work:

1. Generator Network: The generator takes random noise as input and generates synthetic data samples. It tries to learn the underlying patterns and distribution of the training data to produce realistic samples. Initially, the generator produces random and meaningless outputs, but as it trains, it becomes better at generating realistic samples.
2. Discriminator Network: The discriminator receives both real data samples from the training set and fake samples generated by the generator. Its goal is to correctly classify whether a given sample is real or fake. The discriminator is trained to improve its ability to distinguish between real and fake samples.
3. Adversarial Training: The generator and discriminator are trained in alternating steps. First, the discriminator is trained on a batch of real and fake samples, optimizing its parameters to better classify them. Then, the generator is trained using the gradients from the discriminator to update its parameters, aiming to generate samples that can fool the discriminator. This adversarial process continues until both networks reach a point of equilibrium, where the generator produces samples that are indistinguishable from real data, and the discriminator cannot differentiate between real and fake samples.

The training process of GANs is challenging and can be unstable. It requires careful tuning of hyperparameters, architectures, and training strategies to ensure that the generator and discriminator learn effectively. Common techniques used to stabilize GAN training include adjusting the learning rates, using different loss functions, and employing regularization techniques like batch normalization.

GANs have been successfully applied in various domains, such as image generation, text generation, video synthesis, and even in improving the quality of existing data. They have shown impressive results in generating realistic and diverse samples, making them a powerful tool in the field of deep learning.