In most objective functions comparing a learned and source probability distribution, KL divergence is used to measure their dissimilarity. What advantages does KL divergence have over true metrics like Wasserstein (earth mover’s distance), and Bhattacharyya? Is its asymmetry actually a desired property because the fixed source distribution should be treated differently compared to a learned distribution?