Clustering Learning for Robotic Vision

Eugenio Culurciello; Jordan Bates; Aysegul Dundar; Jose Carrasco; Clement Farabet

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arXiv · 1301.2820

Clustering Learning for Robotic Vision

Eugenio Culurciello·Jordan Bates·Aysegul Dundar·Jose Carrasco·Clement Farabet

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Abstract

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.

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Eugenio Culurciello, Jordan Bates, Aysegul Dundar, Jose Carrasco, Clement Farabet. 2013-03-13. Clustering Learning for Robotic Vision. https://arxiv.org/abs/1301.2820

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