Generative Models
Generative adversarial network (GAN)
A generative adversarial network (GAN) is a type of neural network architecture designed for generative modeling. GANs consist of two neural networks, a generator and a discriminator, that are trained in an adversarial manner: the generator tries to create realistic data samples, while the discriminator tries to distinguish between real and generated samples.
Explanation
GANs work through a competitive process between the generator and discriminator networks. The generator takes random noise as input and attempts to transform it into data that resembles the real data distribution. The discriminator, on the other hand, receives both real data samples from the training dataset and fake data samples from the generator. Its task is to classify each input as either real or fake. During training, the generator is updated to become better at fooling the discriminator, and the discriminator is updated to become better at identifying fake samples. This adversarial process drives both networks to improve, ideally leading the generator to produce highly realistic data. GANs have been used for various tasks, including image generation, image-to-image translation, and data augmentation. However, training GANs can be challenging due to issues like mode collapse (where the generator only produces a limited variety of outputs) and instability.