Evolving Deep Convolutional Variational Autoencoders for Image Classification

Variational autoencoders (VAEs) have demonstrated their superiority in unsupervised learning for image processing in recent years. The performance of the VAEs highly depends on their architectures, which are often handcrafted by the human expertise in deep neural networks (DNNs). However, such exper...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 25; no. 5; pp. 815 - 829
Main Authors:	Chen, Xiangru, Sun, Yanan, Zhang, Mengjie, Peng, Dezhong
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.10.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation algorithms Artificial neural networks Benchmarks Computer architecture Computer science Convolutional variational autoencoder Datasets Encoding End users evolving deep learning genetic algorithm (GA) Genetic algorithms Image classification Image processing Machine learning neural architecture search (NAS) Task analysis Training
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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