Iterative autoencoding and clustering for unsupervised feature representation

Songlin Du, Takeshi Ikenaga

研究成果

2 被引用数 (Scopus)

抄録

Unsupervised feature representation is a challenging problem in machine learning and computer vision. Since manual labels are unavailable for training, it is difficult to reduce the gap between learned features and image semantics. This paper proposes an iterative autoencoding and clustering approach, which consists of an autoencoding sub-network and a classification sub-network, for unsupervised feature representation. On one hand, the autoencoding sub-network maps images to features. On the other hand, using the features generated by the autoencoding sub-network, the classification sub-network maps the features to classes and estimates pseudo labels by clustering the features simultaneously. Through iterations between the feature representation and the pseudo-labels-supervised classification, the gap between features and image semantics is reduced. Experimental results on handwritten digits recognition and objects classification prove that the proposed approach achieves state-of-the-art performance compared with existing methods.

本文言語English
ホスト出版物のタイトル2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728103976
DOI
出版ステータスPublished - 2019
イベント2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
継続期間: 2019 5 262019 5 29

出版物シリーズ

名前Proceedings - IEEE International Symposium on Circuits and Systems
2019-May
ISSN(印刷版)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
国/地域Japan
CitySapporo
Period19/5/2619/5/29

ASJC Scopus subject areas

  • 電子工学および電気工学

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