What main aspect do generative adversarial networks focus on?

Generative Adversarial Networks (GANs) primarily focus on a competitive framework involving two neural networks: the generator and the discriminator. The generator's role is to create synthetic data that mimics real data, while the discriminator's task is to differentiate between real data and the synthetic data generated by the generator. This adversarial process allows both networks to improve iteratively; as the generator enhances its ability to produce realistic data, the discriminator simultaneously improves its ability to distinguish real from fake. This competition drives the overall model to generate high-quality data output, making option B the correct choice.

The other options don't align with the fundamental workings of GANs. Optimizing real-time data processing pertains more to data pipeline efficiency, analyzing historical data trends focuses on understanding past data rather than generating new instances, and reducing noise in data generally involves pre-processing steps rather than the creative generation of new data.