Self-Guided Belief Propagation - a Homotopy Continuation Method
- Sat, Oct 01, 2022
Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models. In this work we show how one can improve the performance of BP by solving a sequence of models that starts with independent variables. We term this approach self-guided belief propagation (SBP) and theoretically demonstrate that SBP finds the global optimum of the Bethe approximation for attractive models where all variables favor the same state .Moreover, we apply SBP to various graphs (random ones, and graphs corresponding to problems in wireless communications and computer vision) and show that (i) SBP is superior in terms of accuracy whenever BP converges, and (ii) SBP obtains a unique, stable, and accurate solution whenever BP does not converge.
More information can be found in our [paper][https://ieeexplore.ieee.org/abstract/document/9852264]
Figure: Image corrupted with salt and pepper noise. BP reduces the noise but struggles with reconstructing the boundary regions; SBP reduces the noise as well while preserving the objects.
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