ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-label Classification

Sima Behpour, Wei Xing, Brian D. Ziebart
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Many structured prediction tasks arising in computer vision and natural language processing tractably reduce to making minimum cost cuts in graphs with edge weights learned using maximum margin methods. Unfortunately, the hinge loss used to construct these methods often provides a particularly loose bound on the loss function of interest (e.g., the Hamming loss). We develop Adversarial Robust Cuts (ARC), an approach that poses the learning task as a minimax game between predictor and "label
more » ... oximator" based on minimum cost graph cuts. Unlike maximum margin methods, this game-theoretic perspective always provides meaningful bounds on the Hamming loss. We conduct multi-label and semi-supervised binary prediction experiments that demonstrate the benefits of our approach.
doi:10.1109/cvprw.2018.00255 dblp:conf/cvpr/Behpour18 fatcat:4ehxzmk7trddfpgmqtydxmuo7a