Title: Y-shaped Generative Flows
Primary area: generative models
Keywords: Generative Flows, Normalizing Flows, Optimal Transport

Abstract: Modern generative models usually assume V-shaped flows: The probability mass moves from the source to the targets along straight separate paths. We argue that this bias underrepresents hierarchical structure in real data, where classes share ancestral pathways. We propose Y-shaped generative flows, a alternative derived from branched optimal transport, which favors moving particles together before branching to specialized targets. We developed a scalable neural network-based formulation trained with continuous normalizing flows. In synthetic, image, and biology datasets, our method produces hierarchy-aware trajectories while matching strong baselines.

TL;DR: Generative models with branched flow structures