Abstract
In this paper, we propose a novel autoencoder variant, smooth autoencoder (SmAE), to learn robust and discriminative feature representations. Different from conventional autoencoders which reconstruct each sample from its encoding, we use the encoding of each sample to reconstruct its local neighbors. In this way, the learned representations are consistent among local neighbors and robust to small variations of the inputs. When trained with supervisory information, our approach forces samples from the same class to become more compact in the vicinity of data manifolds in the new representation space, where the samples are easier to be discriminated. Experimental results verify the effectiveness of the representations learned by our approach in image classification and face recognition tasks.
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
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221–231 (2013)
Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: NIPS (2013)
Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Le, Q.V., Ng, A.Y.: On optimization methods for deep learning. In: ICML (2011)
Ranzato, M., Boureau, Y.L., LeCun, Y.: Sparse feature learning for deep belief networks. In: NIPS (2007)
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: NIPS (2012)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML (2008)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: ICML (2011)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003)
Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: NIPS (2009)
Balasubramanian, K., Yu, K., Lebanon, G.: Smooth sparse coding via marginal regression for learning sparse representations. In: ICML (2013)
Weston, J., Ratle, F., Mobahi, H., Collobert, R.: Deep learning via semi-supervised embedding. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 639–655. Springer, Heidelberg (2012)
Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length, and helmholtz free energy. In: NIPS (1994)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Larochelle, H., Erhan, D., Courville, A., Bergstra, J., Bengio, Y.: An empirical evaluation of deep architectures on problems with many factors of variation. In: ICML (2007)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23, 643–660 (2001)
Martinez, A.M.: The ar face database. Technical report 24 (1998)
Aharon, M., Elad, M., Bruckstein, A.: K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)
Jiang, Z., Lin, Z., Davis, L.S.: Learning a discriminative dictionary for sparse coding via label consistent k-svd. In: CVPR (2011)
Guo, H., Jiang, Z., Davis, L.S.: Discriminative dictionary learning with pairwise constraints. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 328–342. Springer, Heidelberg (2013)
Wang, Z., Yang, J., Nasrabadi, N., Huang, T.: A max-margin perspective on sparse representation-based classification. In: CVPR (2013)
Acknowledgement
This work is partially supported by the National Natural Science Foundation of China under contract No. 61390515, 61272319, and 61202297 and Natural Science Foundation of Fujian Province under contract No.2013J01239.
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
Liang, K., Chang, H., Cui, Z., Shan, S., Chen, X. (2015). Representation Learning with Smooth Autoencoder. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-16808-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16807-4
Online ISBN: 978-3-319-16808-1
eBook Packages: Computer ScienceComputer Science (R0)