Abstract
Recurrent neural networks (RNNs) have proved effective at one dimensional sequence learning tasks, such as speech and online handwriting recognition. Some of the properties that make RNNs suitable for such tasks, for example robustness to input warping, and the ability to access contextual information, are also desirable in multi-dimensional domains. However, there has so far been no direct way of applying RNNs to data with more than one spatio-temporal dimension. This paper introduces multi-dimensional recurrent neural networks, thereby extending the potential applicability of RNNs to vision, video processing, medical imaging and many other areas, while avoiding the scaling problems that have plagued other multi-dimensional models. Experimental results are provided for two image segmentation tasks.
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
Bridle, J.S.: Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Fogleman-Soulie, F., Herault, J. (eds.) Neurocomputing: Algorithms, Architectures and Applications, pp. 227–236. Springer, Heidelberg (1990)
Gers, F., Schraudolph, N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research 3, 115–143 (2002)
Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the International Conference on Machine Learning, ICML 2006, Pittsburgh, USA (2006)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5-6), 602–610 (2005)
Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: Kremer, S.C., Kolen, J.F. (eds.) A Field Guide to Dynamical Recurrent Neural Networks, IEEE Computer Society Press, Los Alamitos (2001)
Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Computation 9(8), 1735–1780 (1997)
Hülsken, F., Wallhoff, F., Rigoll, G.: Facial expression recognition with pseudo-3d hidden markov models. In: Proceedings of the 23rd DAGM-Symposium on Pattern Recognition, pp. 291–297. Springer, London, UK (2001)
Jiten, J., Mérialdo, B., Huet, B.: Multi-dimensional dependency-tree hidden Markov models. In: ICASSP 2006. 31st IEEE International Conference on Acoustics, Speech, and Signal Processing, Toulouse, France, May 14-19, 2006, IEEE Computer Society Press, Los Alamitos (2006)
Joshi, D., Li, J., Wang, J.Z.: Parameter estimation of multi-dimensional hidden markov models: A scalable approach. p. III, pp. 149–152 (2005)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Li, J., Najmi, A., Gray, R.M.: Image classification by a two-dimensional hidden markov model. IEEE Transactions on Signal Processing 48(2), 517–533 (2000)
McCarter, G., Storkey, A.: Air Freight Image Segmentation Database, http://homepages.inf.ed.ac.uk/amos/afreightdata.html
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing 45, 2673–2681 (1997)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR ’03. Proceedings of the Seventh International Conference on Document Analysis and Recognition, p. 958. IEEE Computer Society Press, Washington, DC (2003)
Williams, R.J., Zipser, D.: Gradient-based learning algorithms for recurrent networks and their computational complexity. In: Chauvin, Y., Rumelhart, D.E. (eds.) Back-propagation: Theory, Architectures and Applications, pp. 433–486. Lawrence Erlbaum Publishers, Hillsdale, NJ (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Graves, A., Fernández, S., Schmidhuber, J. (2007). Multi-dimensional Recurrent Neural Networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_56
Download citation
DOI: https://doi.org/10.1007/978-3-540-74690-4_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74689-8
Online ISBN: 978-3-540-74690-4
eBook Packages: Computer ScienceComputer Science (R0)