SDDBMs: Soft Denoising Diffusion Bridge Models
Quick summary
arXiv:2608.08594v1 Announce Type: new Abstract: Diffusion bridge models leverage Doob's \(h\)-transform to construct stochastic transports between arbitrary endpoint distributions, and have shown strong potential in image-to-image translation and restoration. However, most existing bridge models rely on hard endpoint conditioning, which forces the terminal state to match a prescribed target exactly. This hard constraint induces terminal-boundary singularities: the terminal law collapses to a Dirac measure, and the resulting drift coefficients become ill-conditioned near the endpoint. In this p
Key takeaways
- arXiv:2608.08594v1 Announce Type: new Abstract: Diffusion bridge models leverage Doob's \(h\)-transform to construct stochastic transports between arbitrary endpoint distributions, and have shown strong potential in image-to-image translation and restoration.
- However, most existing bridge models rely on hard endpoint conditioning, which forces the terminal state to match a prescribed target exactly.
- This hard constraint induces terminal-boundary singularities: the terminal law collapses to a Dirac measure, and the resulting drift coefficients become ill-conditioned near the endpoint.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “SDDBMs: Soft Denoising Diffusion Bridge Models” may reshape data collection, model training, output accountability and market access.
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