Jensen Shannon Divergence

Jensen-Shannon divergence (JSD) is a measure of the similarity between two probability distributions, offering a symmetric and bounded alternative to other divergence metrics like Kullback-Leibler divergence. Current research focuses on leveraging JSD in diverse applications, including data selection for efficient machine learning, evaluating the performance of generative models and assessing the stability of feature selection algorithms, often within the context of neural networks and other advanced model architectures. The widespread use of JSD stems from its ability to quantify distributional differences robustly and its applicability across various fields, from image analysis and natural language processing to healthcare and materials science.