Which DL architecture uses a generator to create samples and a discriminator to evaluate them?

Which DL architecture uses a generator to create samples and a discriminator to evaluate them?

• A. General adversarial networks (GANs)
• B. Reinforcement learning
• C. Supervised learning
• D. ToM

Answer: (A)

Generative Adversarial Networks operate with two neural networks in a game. The generator creates samples from random noise, aiming to resemble real data. The discriminator evaluates these samples and tries to tell apart real data from generated ones. They train together in a minimax setup: the generator tries to produce more convincing outputs to fool the discriminator, while the discriminator gets better at spotting fakes. This adversarial dynamic is what gives GANs their power for generating realistic data. The other options describe different approaches—reinforcement learning relies on rewards and interactions, supervised learning uses labeled pairs, and ToM is not a standard DL architecture.

Generative Adversarial Networks operate with two neural networks in a game. The generator creates samples from random noise, aiming to resemble real data. The discriminator evaluates these samples and tries to tell apart real data from generated ones. They train together in a minimax setup: the generator tries to produce more convincing outputs to fool the discriminator, while the discriminator gets better at spotting fakes. This adversarial dynamic is what gives GANs their power for generating realistic data. The other options describe different approaches—reinforcement learning relies on rewards and interactions, supervised learning uses labeled pairs, and ToM is not a standard DL architecture.