Ponencia
Clustering learning for robotic vision
Autor/es | Culurciello, Eugenio
Bates, Jordan Dundar, Aysegul Pérez Carrasco, José Antonio Farabet, Clément |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2013 |
Fecha de depósito | 2019-01-18 |
Publicado en |
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Resumen | We present the clustering learning technique applied to multi-layer feedforward
deep neural networks. We show that this unsupervised learning technique can
compute network filters with only a few minutes and a much reduced ... We present the clustering learning technique applied to multi-layer feedforward deep neural networks. We show that this unsupervised learning technique can compute network filters with only a few minutes and a much reduced set of parameters. The goal of this paper is to promote the technique for general-purpose robotic vision systems. We report its use in static image datasets and object tracking datasets. We show that networks trained with clustering learning can outperform large networks trained for many hours on complex datasets. |
Cita | Culurciello, E., Bates, J., Dundar, A., Pérez Carrasco, J.A. y Farabet, C. (2013). Clustering learning for robotic vision. En International Conference on Learning Representations, Scottsdale, Arizona. |
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1301.2820.pdf | 749.1Kb | [PDF] | Ver/ | |