Multi-dimensional Recurrent Neural Networks
Alex Gravesa, Santiago Fernándeza, J. Schmidhubera b
- aIDSIA , Galleria 2, 6928 Manno-Lugano, Switzerland
- bTU Munich, Boltzmannstr. 3, 85748 Garching, Munich, Germany
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
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.
TL;DRMulti-dimensional recurrent neural networks are introduced, 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. (Semantic Scholar)