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, Volume 78, Issue 1, pp 573–589 | Cite as

Coupled-learning convolutional neural networks for object recognition

  • Chunyan XuEmail author
  • Jian Yang
  • Junbin Gao
Article

Abstract

Recently, convolutional neural networks (CNN) have been attracting considerable attention in various computer vision tasks. Motivated by neuroscience, CNN has several similar properties with the learning process of human brain. A prominent difference is that each CNN is an independent learning process while the effective interaction/communication between people can play important role in the human visual system. Inspired by this fact, we proposed a novel Coupled-learning Convolutional Neural Network (Co-CNN) for the task of object recognition, which boosts its discriminative capability by employing the dynamic interaction between neural networks. Contrary to existing network architectures posing the network optimization problem as an isolated learning process, the intuition behind the Co-CNN framework is that the coupled learning mechanism may prevent the algorithm away from over-fitting to one or more particular objective functions. The proposed Co-CNN framework has three unique characteristics: (1) Co-CNN, which is a novel deep network learning framework, can simultaneously optimize both neural networks with same/different structures. (2) The learned semantic information, which can be gradually mined from neural networks, is employed to guide the communication between neural networks. (3) Co-CNN well incorporates the coupled-learning mechanism into the process of learning neural networks, and then further improve the recognition performance of neural networks by adopting the learned semantic information. Comprehensive evaluations on five benchmark datasets (CIFAR-10, CIFAR-100, MNIST, SVHN and Imagenet) well demonstrate the significant superiority of our proposed Co-CNN framework over other existing algorithms.

Keywords

Coupled-learning mechanism Convolutional neural network Object recognition Semantic information 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61602244 and 61502235) and partially sponsored by CCF-Tencent Open Research Fund.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.Discipline of Business Analytics, University of Sydney Business SchoolUniversity of SydneySydneyAustralia

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