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Proceedings Paper

New pruning techniques for constructive neural networks with application to image compression
Author(s): Liying Ma; Khashayar Khorasani
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Paper Abstract

Image compression is an important research domain in image processing. Recently, several neural netowkr (NN) based schemes developed in this are. In particular, constructive feed-forward neural networks have been attempted by many researchers to this problem. The constructive NN-based schemes are promising given their lower training cost, satisfactory performance and automatic determination of proper network size. In this paper, we first consider a NN- based technique that uses a constructive one-hidden-layer FNN for image compression. In standard NN-based schemes when a new hidden unit is added to the net the whole net is retrained while in this scheme the input-side weights are first trained and then all the network output-side weights are adjusted, resulting in a considerably less computational efforts. Next, two pruning techniques are proposed to remove the unnecessary input-side weights during the network construction, without sacrificing the performance of the network, to yield a smaller and a more economical network. To confirm the effectiveness of the prosed techniques, we have applied them to both regression problems and image compression. It has been found that a significant number of weights can be pruned without degenerating the network performance.

Paper Details

Date Published: 4 August 2000
PDF: 11 pages
Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395080
Show Author Affiliations
Liying Ma, Concordia Univ. (Canada)
Khashayar Khorasani, Concordia Univ. (Canada)

Published in SPIE Proceedings Vol. 4052:
Signal Processing, Sensor Fusion, and Target Recognition IX
Ivan Kadar, Editor(s)

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