Why might a developer choose a generative model over a discriminative model?

A developer might choose a generative model over a discriminative model primarily for generating new data points from learned distributions. Generative models, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), excel at capturing the underlying distribution of the data they are trained on. This ability allows them to create entirely new samples that resemble the training data, which is particularly useful in applications such as creative content generation, data augmentation, and simulations.

In contrast, discriminative models are focused specifically on learning the boundary between different classes within existing data, rather than modeling the full distribution. They excel in classification tasks and often rely on labeled data. While they can improve classification accuracy, they do not have the capability to generate new data points or capture the broader patterns found in the entire dataset. This makes generative models the preferred choice in scenarios where the creation of new, synthetic data is required.