An Introduction to Deep Learning for Generative Models
An Introduction to Deep Learning for Generative Models What Are Generative Models? Generative models are a type of machine learning model that create new data samples that resemble the data they were trained on. For example, if a generative model is trained on images of faces, it can produce completely new, realistic-looking faces that never existed before. In contrast to discriminative models (which classify or predict labels), generative models try to learn the underlying distribution of the data, allowing them to generate new, similar examples. How Deep Learning Powers Generative Models Deep learning has made generative models more powerful by using neural networks—especially deep ones—to handle complex, high-dimensional data like images, sound, or text. These models can learn rich patterns and structures, enabling them to generate creative and realistic content. Popular Types of Deep Generative Models 1. Autoencoders (AEs) and Variational Autoencoders (VAEs) Autoencoders learn to c...