The Principles of Diffusion Models
13 days ago
- #deep-learning
- #diffusion-models
- #machine-learning
- Diffusion models are based on gradually corrupting data into noise and then learning to reverse this process.
- Three main views of diffusion models: variational (noise removal), score-based (gradient learning), and flow-based (velocity field).
- The common backbone is a time-dependent velocity field that transforms noise into data via a continuous trajectory.
- Applications include controllable generation, efficient numerical solvers, and flow-map models for direct mappings.
- The monograph provides a conceptual and mathematically grounded understanding for those with basic deep-learning knowledge.