Posterior Augmented Flow Matching
Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-dimensional images, each training sample supervises only a single trajectory and intermediate point, yielding an extremely sparse and high-variance training signal. This under-constrained supervision can cause flow collapse, where the learned dynamics memorize specific source-target pairings, mapping diverse inputs to overly similar outputs, failing to generalize. We introduce Posterior-Augmented Flow Matching (PAFM), a theoretically grounded generalization of FM that replaces single-target supervision with an expectation over an approximate...
| Authors | George Stoica, Sayak Paul, Matthew Wallingford et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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