Which of the following statements best describes the fundamental principle behind how Generative Adversarial Networks $($GANs$)$ work?

a$.$ GANs consist of a single neural network that learns to generate new

data that mimics a training dataset.

b$.$ GANs consist of two networks, a generator and a learner, where the

generator creates new training data for the learner.

c$.$ GANs consist of a single neural network that predicts new data by

cross out

learning from a labeled dataset

d$.$ GANs consist of two neural networks, a generator and a

discriminator, which compete against each other during training.