DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields

Abstract:Basic probabilistic models form the basis of modern deep learning largely by defining the final activation layer of a network and allowing the networks outputs to be interpreted through a probabilistic lens. By increasing the complexity of these final activation layers to include features such as spatial regularisation, allow for these models to improve in performance without requiring significantly more data. The challenge however is to ensure that these more complex probabilistic graphical models adapt well to being used in tandem with deep learning, that is, ensuring they converge and have meaningful derivatives. The goal of this paper is to show two algorithms with provable convergence properties to allow this type of regularisation for image and signal classification models. Through both rigorous mathematical analysis and in silico experiments, we can show the differing performance between these solution types.

Machine Learning (cs.LG)

arXiv:2610.16546 [cs.LG]

(or arXiv:2610.16546v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.16546 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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