Unbiased Diffusion Variational Inversion via Principled Posterior Matching
Existing score-based methods for inverse problems often resort to approximate minimization of the KL divergence between the inversion distribution and the Bayesian posterior. Such an approximation leads to severe mode collapse and unreliable uncertainty quantification. In this paper, we propose Principled Posterior Matching (PPM), a framework that returns to the fundamentals of variational inference, rather than using tricky approximations. Instead of relying on heuristic approximations, we rigorously formulate the exact optimization of the KL divergence via the integration of Fisher divergence. We derive a tractable, equivalent gradient form of this integral, enabling precise optimizatio...
| Authors | Weimin Bai, Yuxuan Gu, Yifei Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 May 2026 |
| Last updated at source | 26 June 2026 |
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