Definition
Generative models are algorithms designed to understand the patterns and structures within a given dataset so they can create new data instances that resemble the original. Unlike discriminative models that classify data, generative models learn the joint probability distribution P(X,Y). Common architectures include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models. They are fundamental to modern AI applications involving image synthesis, text generation, and audio creation.
Summary
A type of machine learning model that learns the underlying distribution of data to generate new, synthetic samples similar to the training data.
Key Concepts
- Probability Distribution
- Synthetic Data
- Latent Space
- Training Objective
Use Cases
- Image generation and editing
- Natural language processing
- Data augmentation for small datasets