AI RESEARCH
Paired Wasserstein Autoencoders for Conditional Sampling
arXiv CS.LG
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ArXi:2412.07586v2 Announce Type: replace Generative autoencoders learn compact latent representations of data distributions through jointly optimized encoder--decoder pairs. In particular, Wasserstein autoencoders (WAEs) minimize a relaxed optimal transport (OT) objective, where similarity between distributions is measured through a cost-minimizing joint distribution (OT coupling). Beyond distribution matching, neural OT methods aim to learn mappings between two data distributions induced by an OT coupling.