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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Evolutionary Structure Optimization of Convolutional Neural Networks for Deployment on Resource Limited Systems

verfasst von : Qianyu Zhang, Bin Li, Yi Wu

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

Convolutional neural networks (CNNs) have achieved great success in various computer vision tasks. However, trial and error are still the most adopted way to design the structure of neural networks, which are time consuming. In this work, a population based evolutionary structure optimization approach of CNNs for deployment on resource limited systems is proposed. The method evolved several different kinds of individuals together in a way called natural selection. Evolutionary operators in conventional genetic algorithms are well defined based on structure design problem. Objectives of minimizing space cost or time cost of one deep neural network is considered in optimization process. Experiments on MNIST datasets show that the proposed method can evolve networks with state-of-the-art accuracy and have low storage or time cost, which give inspiration to hand-made structure by the obtained network structure.

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Vorheriges Kapitel A Grouping Genetic Algorithm Based on the GES Local Search for Pickup and Delivery Problem with Time Windows and LIFO Loading
Nächstes Kapitel Research on Vehicle Routing Problem and Its Optimization Algorithm Based on Assembled Building
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Metadaten
Titel
Evolutionary Structure Optimization of Convolutional Neural Networks for Deployment on Resource Limited Systems
verfasst von
Qianyu Zhang
Bin Li
Yi Wu
Copyright-Jahr
2018
Buch
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-95932-0

Electronic ISBN: 978-3-319-95933-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-95933-7_82

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