What consists of two AI models, a generator and a discriminator?

• A. Transformer
• B. Generative Adversarial Network
• C. Recurrent Neural Network
• D. Convolutional Neural Network

Answer: (B)

Generative Adversarial Networks are built from two models, a generator and a discriminator, that learn together in a competitive setup. The generator creates synthetic data with the aim of resembling real data, while the discriminator attempts to distinguish real data from the generated data. They engage in a minimax game: the generator tries to fool the discriminator, and the discriminator tries to correctly identify real versus fake. Through this adversarial training, both networks improve—the generator becomes capable of producing highly realistic samples. This framework is used for tasks like image synthesis and other data generation. The other options describe single-model architectures (Transformers for attention-based sequence processing, Recurrent Neural Networks for sequential data, and Convolutional Neural Networks for spatial pattern recognition) and do not describe a system of two models trained in opposition.