Perron--Frobenius Operator Matching for Generative Modeling

In three linesPFOM (Perron-Frobenius Operator Matching) is a generative framework unifying flow, diffusion, and jump models via the integral PF operator. Authors prove only Kullback-Leibler divergence preserves equality between density-level and sample-conditioned objectives. Nesterov-accelerated training and sampling stabilize discretization and accelerate convergence.

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