@inproceedings{92011af6ce244542971433108034cf7d,
title = "Contractive rectifier networks for nonlinear maximum margin classification",
abstract = "To find the optimal nonlinear separating boundary with maximum margin in the input data space, this paper proposes Contractive Rectifier Networks (CRNs), wherein the hidden-layer transformations are restricted to be contraction mappings. The contractive constraints ensure that the achieved separating margin in the input space is larger than or equal to the separating margin in the output layer. The training of the proposed CRNs is formulated as a linear support vector machine (SVM) in the output layer, combined with two or more contractive hidden layers. Effective algorithms have been proposed to address the optimization challenges arising from contraction constraints. Experimental results on MNIST, CIFAR-10, CIFAR-100 and MIT-67 datasets demonstrate that the proposed contractive rectifier networks consistently outperform their conventional unconstrained rectifier network counterparts",
keywords = "Deep learning, Pattern recognition, Computer vision",
author = "Senjian An and Munawar Hayat and Salam Khan and Mohammed Bennamoun and Farid Boussaid and Ferdous Sohel",
year = "2015",
doi = "10.1109/iccv.2015.289",
language = "English",
isbn = "9781467383912",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "2515--2523",
editor = "Ruzena Bajcsy and Greg Hager and Yi Ma",
booktitle = "2015 International Conference on Computer Vision, ICCV 2015",
address = "United States",
note = "2015 IEEE International Conference on Computer Vision, ICCV 2015 ; Conference date: 11-12-2015 Through 18-12-2015",
}