Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

CVPR 2013 Aurelien LucchiYunpeng LiPascal Fua

We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphical models, such as loopy MRFs and CRFs commonly used in image segmentation, where exact inference is intractable and the most violated constraints can only be approximated, voiding the optimality guarantees of the structured SVM's cutting plane algorithm as well as reducing the robustness of existing subgradient based methods... (read more)

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