Generative Adversarial Networks (GANs) are a type of neural network architecture that are used for generating new data samples that are similar to a given dataset. GANs consist of two neural networks: a generator network, which generates new data samples, and a discriminator network, which tries to distinguish between the generated samples and the real ones.
During training, the generator network is trained to generate samples that can fool the discriminator network, while the discriminator network is trained to correctly classify the samples as real or fake. The two networks are trained simultaneously in an adversarial manner, where the generator tries to produce samples that are indistinguishable from the real ones, while the discriminator tries to distinguish between the real and fake samples.
One of the key advantages of GANs is their ability to generate new data samples that are similar to the training data, but different in some way. This makes them well-suited to tasks such as image generation, where the goal is to generate new images that are similar to a given dataset, but with some variation.
Several variants of GANs have been developed to improve their performance on specific tasks, such as conditional GANs, where the generator is conditioned on a specific input, or style transfer GANs, where the goal is to transfer the style of one image to another.
GANs have been used in a variety of applications, such as image and video generation, image inpainting, and text-to-image synthesis. They have also been used in scientific domains such as drug discovery and protein structure prediction.