Generative Adversarial Networks

GANs consist of a generator network that creates synthetic data and a discriminator network that evaluates the authenticity of generated samples. Through adversarial training, both networks improve - the generator creates more realistic outputs while the discriminator becomes better at detection. This architecture enables the creation of highly realistic synthetic content across various domains including images, video, and audio.

Image synthesis, data augmentation, style transfer, synthetic data generation