Sun 2019 Particle swarm optimization-based flexible convolutional auto-encoder .pdf (640.92 kB)
Download file

A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

Download (640.92 kB)
journal contribution
posted on 24.03.2021, 01:23 by Y Sun, Bing XueBing Xue, Mengjie ZhangMengjie Zhang, GG Yen
© 2012 IEEE. Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention. We test the proposed approach on four extensively used image classification data sets. Experimental results show that our proposed approach in this paper significantly outperforms the peer competitors including the state-of-the-art algorithms. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

History

Preferred citation

Sun, Y., Xue, B., Zhang, M. & Yen, G. G. (2019). A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification. IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2295-2309. https://doi.org/10.1109/TNNLS.2018.2881143

Journal title

IEEE Transactions on Neural Networks and Learning Systems

Volume

30

Issue

8

Publication date

01/08/2019

Pagination

2295-2309

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Contribution type

Article

ISSN

2162-237X

eISSN

2162-2388

Language

en