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2016 | OriginalPaper | Chapter

3D Object Recognition Using Convolutional Neural Networks with Transfer Learning Between Input Channels

Author : Luís A. Alexandre

Published in: Intelligent Autonomous Systems 13

Publisher: Springer International Publishing

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Abstract

RGB-D data is getting ever more interest from the research community as both cheap cameras appear in the market and the applications of this type of data become more common. A current trend in processing image data is the use of convolutional neural networks (CNNs) that have consistently beat competition in most benchmark data sets. In this paper, we investigate the possibility of transferring knowledge between CNNs when processing RGB-D data with the goal of both improving accuracy and reducing training time. We present experiments that show that our proposed approach can achieve both these goals.

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Footnotes
2
Since there are too many parameters to present here, the configuration files used can be obtained online: www.​di.​ubi.​pt/​~lfbaa/​pubs/​IAS-13.​zip. The lists with the names of the files used in the training, validation, and test sets are also there. This allows for our experiments to be reproduced.
 
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Metadata
Title
3D Object Recognition Using Convolutional Neural Networks with Transfer Learning Between Input Channels
Author
Luís A. Alexandre
Copyright Year
2016
Book
Intelligent Autonomous Systems 13

Print ISBN: 978-3-319-08337-7

Electronic ISBN: 978-3-319-08338-4

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-08338-4_64

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