Step 1: Understanding the concept.
The question refers to a type of AI model that learns in a unique way by generating and evaluating data, commonly known as Generative Adversarial Networks (GANs).
Step 2: Components of GAN.
• Generator: Creates new data samples similar to training data.
• Discriminator: Evaluates whether the generated data is real or fake.
Step 3: Evaluating options.
• (A): Correct. GAN consists of a generator and a discriminator.
• (B): Used in autoencoders.
• (C): Refers to NLP models.
• (D): Types of neural networks, not components of GAN.
Step 4: Conclusion.
Thus, the two main parts are generator and discriminator.
Final Answer: A generator and a discriminator.