Seminar: Conditional Flow Matching

appliedAI Institute for Europe gGmbH

The TransferLab Seminar (transferlab.ai/seminar) is a platform where researchers and engineers share and discuss recent advances in AI/ML.

Conditional Flow Matching: Simulation-Free Dynamic Optimal TransportAlex Tong, Postdoctoral Fellow, MILA AbstractContinuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their simulation-based maximum likelihood training. We introduce the generalized conditional flow matching (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schrödinger bridge inference. The appliedAI Institute for Europe gGmbH is supported by the KI-Stiftung Heilbronn gGmbH.

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Seminar: Conditional Flow Matching

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Seminar: Conditional Flow Matching

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