Kullback-Leibler (KL) divergence
Concept
A term in the VAE loss function that regularizes the encoder's latent distribution to be close to a fixed prior distribution (e.g., standard normal), helping to structure the latent space.
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Videos Mentioning Kullback-Leibler (KL) divergence
Stanford CME295 Transformers & LLMs | Autumn 2026 | Lecture 4 - Reinforcement Learning with LLMs
Stanford Online
A measure of how one probability distribution diverges from a second, expected probability distribution, used in RL to penalize policies that deviate too much from a reference policy.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 4 - Latent Space & Guidance
Stanford Online
A term in the VAE loss function that regularizes the encoder's latent distribution to be close to a fixed prior distribution (e.g., standard normal), helping to structure the latent space.