What are the two main components of GANs?

The main components of Generative Adversarial Networks (GANs) are the Generator and the Discriminator. The Generator is responsible for creating new data instances, essentially trying to generate data that resembles the training data. It learns to produce outputs that can pass for real data, improving its capabilities through feedback from the discriminator.

The Discriminator, on the other hand, is tasked with distinguishing between real data (from the training dataset) and fake data (generated by the Generator). It provides critical feedback to the Generator on how well it is performing in terms of generating realistic data.

Together, these two elements engage in a competitive process, where the Generator aims to improve its ability to create convincing data, while the Discriminator becomes increasingly skilled at detecting fakes. This adversarial training strategy is what makes GANs unique and powerful for generating high-quality synthetic data. Understanding this interplay between the Generator and Discriminator is essential for grasping the foundational principles of GANs.