The Principles of Diffusion Models

Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

A generative model that learns to denoise data, achieving state-of-the-art image generation (Stable Diffusion, DALL-E 2).

A measure of uncertainty or randomness in a random variable from information theory.

Kullback-Leibler divergence - a measure of how one probability distribution differs from another.

Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

An unsupervised neural network that learns to compress data into a latent representation and reconstruct it, useful for dimensionality reduction.

A generative model that learns a probabilistic latent space, allowing sampling of new data points similar to training data.

Hidden or unobserved variables in a model that influence observed data but aren't directly measured.