What defines a generative adversarial network (GAN)?

A generative adversarial network (GAN) is defined by its unique architecture, which consists of two neural networks that engage in a competitive process. These networks, known as the generator and the discriminator, work against each other to create new data instances that resemble a training dataset. The generator tries to produce data that is indistinguishable from real data, while the discriminator attempts to correctly identify whether a given data instance is real or generated. This adversarial process drives both networks to improve over time, enabling the generation of high-quality synthetic data.

The other choices misrepresent the purpose and functionality of GANs. For instance, a focus solely on data classification oversimplifies the capabilities of GANs, which are primarily used for generating new data rather than just categorizing existing data. While GANs can be utilized in unsupervised learning contexts, they are not limited to this type of learning; indeed, they can also be applied in semi-supervised settings. Lastly, GANs are fundamentally different from models designed for data compression, as their main goal is to create new content rather than reduce the data size.