Abstract
We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms. A max-pooling layer is essential to learn biologically plausible features consistent with those found by previous approaches. Initializing a CNN with filters of a trained CAE stack yields superior performance on a digit (MNIST) and an object recognition (CIFAR10) benchmark.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Behnke, S.: Hierarchical Neural Networks for Image Interpretation. LNCS, vol. 2766, pp. 1–13. Springer, Heidelberg (2003)
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Neural Information Processing Systems, NIPS (2007)
Cireşan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: High-Performance Neural Networks for Visual Object Classification. ArXiv e-prints, arXiv:1102.0183v1 (cs.AI) (Febuary 2011)
Ciresan, D.C., Meier, U., Masci, J., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: International Joint Conference on Artificial Intelligence, IJCAI (to appear 201I)
Coates, A., Lee, H., Ng, A.: An analysis of single-layer networks in unsupervised feature learning. Advances in Neural Information Processing Systems (2010)
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P.: Why Does Unsupervised Pre-training Help Deep Learning? Journal of Machine Learning Research 11, 625–660 (2010)
Fukushima, K.: Neocognitron: A self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comp. 14(8), 1771–1800 (2002)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation (2006)
Hochreiter, S., Schmidhuber, J.: Feature extraction through LOCOCODE. Neural Computation 11(3), 679–714 (1999)
Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology 195(1), 215–243 (1968), http://jp.physoc.org/cgi/content/abstract/195/1/215
Krishevsky, A.: Convolutional deep belief networks on CIFAR-2010 (2010)
Krizhevsky, A.: Learning multiple layers of features from tiny images. Master’s thesis, Computer Science Department, University of Toronto (2009)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F.: A tutorial on energy-based learning. In: Bakir, G., Hofman, T., Schölkopf, B., Smola, A., Taskar, B. (eds.) Predicting Structured Data. MIT Press, Cambridge (2006)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th International Conference on Machine Learning, pp. 609–616 (2009)
Lowe, D.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Norouzi, M., Ranjbar, M., Mori, G.: Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2735–2742 (June 2009), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5206577
Ranzato, M., Boureau, Y., LeCun, Y.: Sparse feature learning for deep belief networks. In: Advances in Neural Information Processing Systems, NIPS 2007 (2007)
Ranzato, M., Fu Jie Huang, Y.L.B., LeCun, Y.: Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: Proc. of Computer Vision and Pattern Recognition Conference (2007)
Ranzato, M., Hinton, G.E.: Modeling pixel means and covariances using factorized third-order boltzmann machines. In: Proc. of Computer Vision and Pattern Recognition Conference, CVPR 2010 (2010)
Scherer, D., Müller, A., Behnke, S.: Evaluation of pooling operations in convolutional architectures for object recognition. In: International Conference on Artificial Neural Networks (2010)
Schmidhuber, J.: Learning factorial codes by predictability minimization. Neural Computation 4(6), 863–879 (1992)
Schmidhuber, J., Eldracher, M., Foltin, B.: Semilinear predictability minimization produces well-known feature detectors. Neural Computation 8(4), 773–786 (1996)
Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: Proc. of Computer Vision and Pattern Recognition Conference (2007)
Simard, P., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, pp. 958–963 (2003)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and Composing Robust Features with Denoising Autoencoders. In: Neural Information Processing Systems, NIPS (2008)
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional Networks. In: Proc. Computer Vision and Pattern Recognition Conference, CVPR 2010 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J. (2011). Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-21735-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21734-0
Online ISBN: 978-3-642-21735-7
eBook Packages: Computer ScienceComputer Science (R0)