Score Matching
A statistical technique for training generative models by teaching them to predict the gradient of the log-probability density of the data.
Think of It Like This
Like teaching someone to navigate a mountain in the dark by only telling them which specific direction points uphill at any given spot.
Instead of maximizing the likelihood directly (which is often intractable), score matching trains the model to map how the data density changes. This forms the mathematical foundation for Score-Based Generative Models and continuous-time Diffusion Models. It allows for highly stable training without requiring complex normalizing constants.