Iterative autoencoding and clustering for unsupervised feature representation

Songlin Du, Takeshi Ikenaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
Publication statusPublished - 2019 Jan 1
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: 2019 May 262019 May 29

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

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

Fingerprint

Labels
Semantics
Computer vision
Learning systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Du, S., & Ikenaga, T. (2019). Iterative autoencoding and clustering for unsupervised feature representation. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings [8702659] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2019.8702659

Iterative autoencoding and clustering for unsupervised feature representation. / Du, Songlin; Ikenaga, Takeshi.

2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8702659 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Du, S & Ikenaga, T 2019, Iterative autoencoding and clustering for unsupervised feature representation. in 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings., 8702659, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019, Sapporo, Japan, 19/5/26. https://doi.org/10.1109/ISCAS.2019.8702659
Du S, Ikenaga T. Iterative autoencoding and clustering for unsupervised feature representation. In 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8702659. (Proceedings - IEEE International Symposium on Circuits and Systems). https://doi.org/10.1109/ISCAS.2019.8702659
Du, Songlin ; Ikenaga, Takeshi. / Iterative autoencoding and clustering for unsupervised feature representation. 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - IEEE International Symposium on Circuits and Systems).
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