Multi-dimensional recurrent neural networks

Alex Graves, Santiago Fernández, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

138 Scopus citations

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. © Springer-Verlag Berlin Heidelberg 2007.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages549-558
Number of pages10
ISBN (Print)9783540746898
DOIs
StatePublished - Jan 1 2007
Externally publishedYes

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