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Communication Dans Un Congrès Année : 2018

Continuous Relaxation of MAP Inference: A Nonconvex Perspective

Résumé

In this paper, we study a nonconvex continuous relaxation of MAP inference in discrete Markov random fields (MRFs). We show that for arbitrary MRFs, this relaxation is tight, and a discrete stationary point of it can be easily reached by a simple block coordinate descent algorithm. In addition, we study the resolution of this relaxation using popular gradient methods, and further propose a more effective solution using a multilinear decomposition framework based on the alternating direction method of multi-pliers (ADMM). Experiments on many real-world problems demonstrate that the proposed ADMM significantly outper-forms other nonconvex relaxation based methods, and compares favorably with state of the art MRF optimization algorithms in different settings.
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Dates et versions

hal-01716514 , version 1 (23-02-2018)
hal-01716514 , version 2 (26-02-2018)

Identifiants

  • HAL Id : hal-01716514 , version 1

Citer

Ð.Khuê Lê-Huu, Nikos Paragios. Continuous Relaxation of MAP Inference: A Nonconvex Perspective. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2018, Salt Lake City, United States. ⟨hal-01716514v1⟩
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