@inproceedings{8a2d4acd215c43d0a712342b512fe137,
title = "Integrating supervised laplacian objective with CNN for object recognition",
abstract = "Methods to improve object recognition accuracies of convolutional neural networks (CNNs) mainly focus on increasing model complexity and training samples, introducing training strategies, etc. Alternatively, in this paper, inspired by {\textquotedblleft}manifolds untangling{\textquotedblright} mechanism from human visual cortex, we propose a novel and general method to improve object recognition accuracies of CNNs by embedding the proposed supervised Laplacian objective (SLO) into a high layer of the models during the training process. The SLO explicitly enforces the learned feature maps with a better within-manifold compactness and betweenmanifold margin, and it can be universally applied to different CNN models. Experiments with shallow and deep models on four benchmark datasets including CIFAR-10, CIFAR-100, SVHN and MNIST demonstrate that CNN models trained with the SLO achieve remarkable performance improvements compared to the corresponding baseline models.",
keywords = "CNN, Object recognitiuon, Supervised laplacian objective",
author = "Weiwei Shi and Yihong Gong and Jinjun Wang and Nanning Zheng",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th Pacific-Rim Conference on Multimedia, PCM 2016 ; Conference date: 15-09-2016 Through 16-09-2016",
year = "2016",
doi = "10.1007/978-3-319-48896-7\_7",
language = "英语",
isbn = "9783319488950",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "64--73",
editor = "Enqing Chen and Yun Tie and Yihong Gong",
booktitle = "Advances in Multimedia Information Processing {\textendash} 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings",
}