Deep learning to ternary hash codes by continuation

Abstract Recently, ithas been observed that {0,±1}‐ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform {−1,1}‐binary codes in image retrieval. To obtain better ternary codes, the authors for the first time propose to jointly learn the features with t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mingrui Chen, Weiyu Li, Weizhi Lu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a5c841e2503147e9bb25a3892e2fa9f6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a5c841e2503147e9bb25a3892e2fa9f6
record_format	dspace
spelling	oai:doaj.org-article:a5c841e2503147e9bb25a3892e2fa9f62021-11-19T05:42:54ZDeep learning to ternary hash codes by continuation1350-911X0013-519410.1049/ell2.12317https://doaj.org/article/a5c841e2503147e9bb25a3892e2fa9f62021-11-01T00:00:00Zhttps://doi.org/10.1049/ell2.12317https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Recently, ithas been observed that {0,±1}‐ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform {−1,1}‐binary codes in image retrieval. To obtain better ternary codes, the authors for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non‐smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes. For the first time, the authors propose to generate ternary hash codes by jointly learning the codes with deep features via a continuation method. Experiments show that the proposed method outperforms existing methods.Mingrui ChenWeiyu LiWeizhi LuWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 24, Pp 925-926 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mingrui Chen Weiyu Li Weizhi Lu Deep learning to ternary hash codes by continuation
description	Abstract Recently, ithas been observed that {0,±1}‐ternary codes, which are simply generated from deep features by hard thresholding, tend to outperform {−1,1}‐binary codes in image retrieval. To obtain better ternary codes, the authors for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non‐smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes. For the first time, the authors propose to generate ternary hash codes by jointly learning the codes with deep features via a continuation method. Experiments show that the proposed method outperforms existing methods.
format	article
author	Mingrui Chen Weiyu Li Weizhi Lu
author_facet	Mingrui Chen Weiyu Li Weizhi Lu
author_sort	Mingrui Chen
title	Deep learning to ternary hash codes by continuation
title_short	Deep learning to ternary hash codes by continuation
title_full	Deep learning to ternary hash codes by continuation
title_fullStr	Deep learning to ternary hash codes by continuation
title_full_unstemmed	Deep learning to ternary hash codes by continuation
title_sort	deep learning to ternary hash codes by continuation
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/a5c841e2503147e9bb25a3892e2fa9f6
work_keys_str_mv	AT mingruichen deeplearningtoternaryhashcodesbycontinuation AT weiyuli deeplearningtoternaryhashcodesbycontinuation AT weizhilu deeplearningtoternaryhashcodesbycontinuation
_version_	1718420392730689536

Deep learning to ternary hash codes by continuation

Ejemplares similares