Which characteristic distinguishes GANs from traditional neural networks?

The defining characteristic that distinguishes GANs, or Generative Adversarial Networks, from traditional neural networks is the presence of two competing networks: a generator and a discriminator. In a GAN, the generator’s role is to create synthetic data that resembles the training data, while the discriminator evaluates the authenticity of the data, determining whether it is real (from the training set) or fake (produced by the generator). This adversarial process is what drives the training of the GAN; the generator improves its outputs to better fool the discriminator, while the discriminator continuously improves its ability to differentiate between real and fake data.

This two-network framework is a fundamental departure from the structure of traditional neural networks, which typically consist of a single model that learns from labeled data to make predictions or classifications. The competitive dynamic within GANs fosters innovation in data generation, allowing them to produce high-quality synthetic outputs that can be indistinguishable from real data, a feature not found in traditional neural network configurations.

The other options refer to aspects common in traditional neural networks or do not capture the unique structure of GANs. Supervised learning is a common approach, but GANs can operate without labels in the traditional sense, focusing instead on the competition between the two networks. Reliance