Generative Adversarial Networks (GANs) are a type of deep neural network architecture introduced by Goodfellow et al. in 2014. GANs are used for unsupervised learning, and are designed to generate new data samples that are similar to a given dataset.
The GAN architecture consists of two neural networks: a generator and a discriminator. The generator takes random noise as input and generates new samples that are intended to resemble the real data. The discriminator takes both real data samples and generated samples as input, and its job is to distinguish between the two types of samples. The goal of the generator is to generate samples that are indistinguishable from the real data, while the goal of the discriminator is to correctly classify the samples as real or fake.
During training, the generator and discriminator networks are trained simultaneously using backpropagation. The generator tries to generate samples that can fool the discriminator, while the discriminator tries to distinguish between real and generated samples. As the training progresses, the generator learns to generate samples that are increasingly similar to the real data, while the discriminator becomes better at distinguishing between real and fake samples.
GANs have been applied to a wide range of applications, including image and video synthesis, music generation, and text generation. GANs are particularly useful for generating samples of high-dimensional data, such as images, that are difficult to model using traditional techniques. One advantage of GANs is that they can learn complex distributions of data without the need for explicit modeling or labeling of the data. However, training GANs can be challenging, as they are prone to instability and mode collapse, where the generator produces only a limited variety of samples.