Clustering Learning for Robotic Vision
| Autor: | Culurciello, Eugenio, Bates, Jordan, Dundar, Aysegul, Pérez Carrasco, José Antonio, Farabet, Clément |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | 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. Code for this paper is available here: https://github.com/culurciello/CL_paper1_code |
| Databáze: | OpenAIRE |
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