Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed by Ian

Goodfellow and his colleagues in 2014. GANs consist of two neural networks, the generator and the

discriminator, which are trained simultaneously through a competitive process. The generator

creates new data instances, while the discriminator evaluates them against real data, effectively

learning to generate new content that is indistinguishable from genuine data.

The generator’s goal is to produce data that is so similar to the real data that the discriminator

cannot tell the difference, while the discriminator’s goal is to correctly identify whether the data it

reviews is real (from the actual dataset) or fake (created by the generator). This competitive process

results in the generator creating highly realistic data.

The Official Dell GenAI Foundations Achievement document likely includes information on GANs, as

they are a significant concept in the field of artificial intelligence and machine learning, particularly

in the context of generative AI12. GANs have a wide range of applications, including image

generation, style transfer, data augmentation, and more.

Feedforward Neural Networks (Option OA) are basic neural networks where connections between

the nodes do not form a cycle. Variational Autoencoders (VAEs) (Option OB) are a type of

autoencoder that provides a probabilistic manner for describing an observation in latent space.

Transformers (Option OD) are a type of model that uses self-attention mechanisms and is widely

used in natural language processing tasks. While these are all important models in AI, they do not

use a competitive setting between two networks to create new data, making Option OC the correct

answer.