What is the main goal of using generative models like VAEs and GANs?

The main goal of using generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) is to generate new, synthetic data that resembles real data. These models are designed to learn the underlying distribution of a dataset so that they can create new samples that share similar characteristics with the original data. This capability is particularly valuable in scenarios where obtaining real data is difficult, expensive, or limited, allowing for the enhancement of training datasets and improving the performance of machine learning models.

Generative models function by capturing patterns, structures, and features of the training data, and they utilize their learned representations to produce novel outputs. This can be used in various applications, such as creating realistic images, generating text, or producing music that mirrors the style of a given dataset. Thus, the strength of generative models lies in their ability to synthesize new instances rather than just classifying or duplicating existing data.