Kullback-Leibler divergence, often abbreviated as KL divergence, quantifies the difference between two probability distributions. It measures how one probability distribution diverges from a second, expected probability distribution. A penalty based on this divergence is commonly used in machine learning and information theory to encourage a model’s learned distribution to resemble a desired or prior distribution. For instance, if a model is meant to generate data similar to a known dataset, a penalty using this divergence can push the model’s generated distribution towards the characteristics of the original dataset.
The imposition of this penalty offers several advantages. It helps to regularize models, preventing overfitting to training data by promoting solutions closer to a prior belief. It facilitates the incorporation of prior knowledge or constraints into the learning process. Historically, this divergence measure originated in information theory as a way to quantify the information lost when one probability distribution is used to approximate another. Its application has since expanded to various fields, including statistical inference, pattern recognition, and deep learning.
