Adversarial loss

Adversarial loss, also known as GAN loss, is a loss function used in Generative Adversarial Networks (GANs). GANs consist of two neural networks: a generator network that generates fake data, and a discriminator network that distinguishes between the fake data generated by the generator and the real data. The goal of the generator is to generate data that is indistinguishable from real data, while the goal of the discriminator is to correctly distinguish between real and fake data.

The adversarial loss is used to train the generator network. It is calculated by feeding the generator's output into the discriminator and then using the discriminator's output as the loss. The generator seeks to minimize this loss, while the discriminator seeks to maximize it. This creates a game-like scenario where the generator and discriminator are in competition with each other, which leads to the generator improving its ability to generate realistic data.