Probability measures get sparse dictionaries: embed once by optimal transport, then train a plain autoencoder
Embedding probability measures into a Hilbert space before learning a sparse dictionary turns training from repeated Wasserstein distance solves into one autoencoder optimization, cutting reconstruction loss by roughly two to three orders of magnitude against Wasserstein dictionary learning under the same time budget.