Abstract
The Neural Network (NN) with Rectified Linear Units (ReLU), has achieved a big success for image classification with large number of labelled training samples. The performance however is unclear when the number of labelled training samples is limited and the size of samples is large. Usually, the Convolutional Neural Network (CNN) is used to process the large-size images, but the unsupervised pre-training method for deep CNN is still progressing slowly. Therefore, in this paper, we first explore the ability of denoising auto-encoder with ReLU for pre-training CNN layer-by-layer, and then investigate the performance of CNN with weight initialized by the pre-trained features for image classification tasks, where the number of training samples is limited and the size of samples is large. Experiments on Caltech-101 benchmark demonstrate the effectiveness of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alain, G., Bengio, Y.: What regularized auto-encoders learn from the data generating distribution. In: ICLR (2013)
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: NIPS (2006)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR (2009)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: International Conference on Artificial Intelligence and Statistics (2011)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Hubel, D., Wiesel, T.: Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophys. 28, 229–289 (1965)
Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: ICCV (2009)
Kavukcuoglu, K., Ranzato, M., LeCun, Y.: Fast inference in sparse coding algorithms with applications to object recognition. Technical report, New York University (2008)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lee, H., Ekanadham, C., Ng, A.Y.: Sparse deep belief net model for visual area v2. In: NIPS (2008)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV (2001)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: ICML (2010)
Ngiam, J., Koh, P.W., Chen, Z., Bhaskar, S., Ng, A.Y.: Sparse filtering. In: NIPS (2011)
Prechelt, L.: Early stopping — but when? In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 53–67. Springer, Heidelberg (2012)
Ranzato, M., Poultney, C., Chopra, S., LeCun, Y.: Efficient learning of sparse representations with an energy-based model. In: NIPS (2006)
Ranzato, M., Boureau, Y.L., LeCun, Y.: Sparse feature learning for deep belief networks. In: NIPS (2007)
Ranzato, M., Huang, F.J., Boureau, Y.L., LeCun, Y.: Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: CVPR (2007)
Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: ICML (2011)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML (2008)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR (2010)
Acknowledgments
The authors would like to thank the editor and the anonymous reviewers for their critical and constructive comments and suggestions. This work was partially supported by the National Science Fund for Distinguished Young Scholars under Grant Nos. 61125305, 91420201, 61472187, 61233011 and 61373063, the Key Project of Chinese Ministry of Education under Grant No. 313030, the 973 Program No. 2014CB349303, Fundamental Research Funds for the Central Universities No. 30920140121005, and Program for Changjiang Scholars and Innovative Research Team in University No. IRT13072.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Luo, W., Li, J., Xu, W., Yang, J. (2015). Learning Sparse Features in Convolutional Neural Networks for Image Classification. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-23989-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23987-3
Online ISBN: 978-3-319-23989-7
eBook Packages: Computer ScienceComputer Science (R0)