Dual Sum-Product Networks Autoencoder for Multi-Label Classification

doi:10.1007/s12204-020-2204-1

Journal of Shanghai Jiao Tong University(Science) ›› 2020, Vol. 25 ›› Issue (5): 665-673.doi: 10.1007/s12204-020-2204-1

Dual Sum-Product Networks Autoencoder for Multi-Label Classification

WANG Shengsheng (王生生), ZHANG Hang (张航), CHEN Juan (陈娟)

(a. College of Computer Science and Technology; b. College of Software, Jilin University, Changchun 130012, China)

出版日期:2020-10-28 发布日期:2020-09-11
通讯作者: ZHANG Hang (张航) E-mail:314362424@qq.com

Dual Sum-Product Networks Autoencoder for Multi-Label Classification

WANG Shengsheng (王生生), ZHANG Hang (张航), CHEN Juan (陈娟)

(a. College of Computer Science and Technology; b. College of Software, Jilin University, Changchun 130012, China)

Online:2020-10-28 Published:2020-09-11
Contact: ZHANG Hang (张航) E-mail:314362424@qq.com

摘要/Abstract

摘要： Sum-product networks (SPNs) are an expressive deep probabilistic architecture with solid theoretical
foundations, which allows tractable and exact inference. SPNs always act as black-box inference machine in many
artificial intelligence tasks. Due to their recursive definition, SPNs can also be naturally employed as hierarchical
feature extractors. Recently, SPNs have been successfully employed as autoencoder framework in representation
learning. However, SPNs autoencoder ignores the model structural duality and trains the models separately and
independently. In this work, we propose a Dual-SPNs autoencoder which designs two SPNs autoencoders to
compose as a dual form. This approach trains the models simultaneously, and explicitly exploits the structural
duality between them to enhance the training process. Experimental results on several multilabel classification
problems demonstrate that Dual-SPNs autoencoder is very competitive against with state-of-the-art autoencoder
architectures.

关键词: sum-product networks (SPNs), representation learning, dual learning, multi-label classification

Abstract: Sum-product networks (SPNs) are an expressive deep probabilistic architecture with solid theoretical
foundations, which allows tractable and exact inference. SPNs always act as black-box inference machine in many
artificial intelligence tasks. Due to their recursive definition, SPNs can also be naturally employed as hierarchical
feature extractors. Recently, SPNs have been successfully employed as autoencoder framework in representation
learning. However, SPNs autoencoder ignores the model structural duality and trains the models separately and
independently. In this work, we propose a Dual-SPNs autoencoder which designs two SPNs autoencoders to
compose as a dual form. This approach trains the models simultaneously, and explicitly exploits the structural
duality between them to enhance the training process. Experimental results on several multilabel classification
problems demonstrate that Dual-SPNs autoencoder is very competitive against with state-of-the-art autoencoder
architectures.

Key words: sum-product networks (SPNs), representation learning, dual learning, multi-label classification

中图分类号:

TP 181

WANG Shengsheng, ZHANG Hang, CHEN Juan . Dual Sum-Product Networks Autoencoder for Multi-Label Classification[J]. Journal of Shanghai Jiao Tong University(Science), 2020, 25(5): 665-673.

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Dual Sum-Product Networks Autoencoder for Multi-Label Classification

Dual Sum-Product Networks Autoencoder for Multi-Label Classification

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