SELF-LEARNING METHOD FOR SEMANTIC FEATURE WITH MAXIMUM GAP, AND COMPUTER DEVICE AND STORAGE MEDIUM

SELF-LEARNING METHOD FOR SEMANTIC FEATURE WITH MAXIMUM GAP, AND COMPUTER DEVICE AND STORAGE MEDIUM

Disclosed in the present invention is a self-learning method for semantic features with the maximum gap. The method comprises: constructing an encoder, wherein a constraint condition for the encoder is that the distance between codes of two pieces of relevant data is less than the distance between c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: HU, Taocheng, YE, Qing, CAO, Yongqiang, LIU, Huili, LEI, Yi
Format: Patent
Sprache:eng
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator HU, Taocheng
YE, Qing
CAO, Yongqiang
LIU, Huili
LEI, Yi
description Disclosed in the present invention is a self-learning method for semantic features with the maximum gap. The method comprises: constructing an encoder, wherein a constraint condition for the encoder is that the distance between codes of two pieces of relevant data is less than the distance between codes of two pieces of irrelevant data; and driving, by using an unlabeled training set, the encoder to train by means of self-learning. Further disclosed in the present invention are a computer device and a computer-readable storage medium. By using the present invention, there is no need to label training data, and an encoder can be trained by directly using original data. In addition, a gap-based constraint condition is also introduced, such that the effect of a training model in practical applications can be effectively guaranteed.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_ZA202309602B</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>ZA202309602B</sourcerecordid><originalsourceid>FETCH-epo_espacenet_ZA202309602B3</originalsourceid><addsrcrecordid>eNqNjEEKwjAQAHvxIOof1ruF0oLgcU02TaCblDRR8VKqxJNoof4fRXyAp4FhmHl26ahReUPorbE1MAXtJCjnoSNGG4wARRiiJziaoIHxZDgy1NhuAK0E4biNgTxIOhhBX9cF57Gmz02ayMtsdhvuU1r9uMjWioLQeRqffZrG4Zoe6dWfsSzKqthti3Jf_dO8AbYkM5c</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>SELF-LEARNING METHOD FOR SEMANTIC FEATURE WITH MAXIMUM GAP, AND COMPUTER DEVICE AND STORAGE MEDIUM</title><source>esp@cenet</source><creator>HU, Taocheng ; YE, Qing ; CAO, Yongqiang ; LIU, Huili ; LEI, Yi</creator><creatorcontrib>HU, Taocheng ; YE, Qing ; CAO, Yongqiang ; LIU, Huili ; LEI, Yi</creatorcontrib><description>Disclosed in the present invention is a self-learning method for semantic features with the maximum gap. The method comprises: constructing an encoder, wherein a constraint condition for the encoder is that the distance between codes of two pieces of relevant data is less than the distance between codes of two pieces of irrelevant data; and driving, by using an unlabeled training set, the encoder to train by means of self-learning. Further disclosed in the present invention are a computer device and a computer-readable storage medium. By using the present invention, there is no need to label training data, and an encoder can be trained by directly using original data. In addition, a gap-based constraint condition is also introduced, such that the effect of a training model in practical applications can be effectively guaranteed.</description><language>eng</language><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240626&amp;DB=EPODOC&amp;CC=ZA&amp;NR=202309602B$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25569,76552</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240626&amp;DB=EPODOC&amp;CC=ZA&amp;NR=202309602B$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HU, Taocheng</creatorcontrib><creatorcontrib>YE, Qing</creatorcontrib><creatorcontrib>CAO, Yongqiang</creatorcontrib><creatorcontrib>LIU, Huili</creatorcontrib><creatorcontrib>LEI, Yi</creatorcontrib><title>SELF-LEARNING METHOD FOR SEMANTIC FEATURE WITH MAXIMUM GAP, AND COMPUTER DEVICE AND STORAGE MEDIUM</title><description>Disclosed in the present invention is a self-learning method for semantic features with the maximum gap. The method comprises: constructing an encoder, wherein a constraint condition for the encoder is that the distance between codes of two pieces of relevant data is less than the distance between codes of two pieces of irrelevant data; and driving, by using an unlabeled training set, the encoder to train by means of self-learning. Further disclosed in the present invention are a computer device and a computer-readable storage medium. By using the present invention, there is no need to label training data, and an encoder can be trained by directly using original data. In addition, a gap-based constraint condition is also introduced, such that the effect of a training model in practical applications can be effectively guaranteed.</description><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjEEKwjAQAHvxIOof1ruF0oLgcU02TaCblDRR8VKqxJNoof4fRXyAp4FhmHl26ahReUPorbE1MAXtJCjnoSNGG4wARRiiJziaoIHxZDgy1NhuAK0E4biNgTxIOhhBX9cF57Gmz02ayMtsdhvuU1r9uMjWioLQeRqffZrG4Zoe6dWfsSzKqthti3Jf_dO8AbYkM5c</recordid><startdate>20240626</startdate><enddate>20240626</enddate><creator>HU, Taocheng</creator><creator>YE, Qing</creator><creator>CAO, Yongqiang</creator><creator>LIU, Huili</creator><creator>LEI, Yi</creator><scope>EVB</scope></search><sort><creationdate>20240626</creationdate><title>SELF-LEARNING METHOD FOR SEMANTIC FEATURE WITH MAXIMUM GAP, AND COMPUTER DEVICE AND STORAGE MEDIUM</title><author>HU, Taocheng ; YE, Qing ; CAO, Yongqiang ; LIU, Huili ; LEI, Yi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_ZA202309602B3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>online_resources</toplevel><creatorcontrib>HU, Taocheng</creatorcontrib><creatorcontrib>YE, Qing</creatorcontrib><creatorcontrib>CAO, Yongqiang</creatorcontrib><creatorcontrib>LIU, Huili</creatorcontrib><creatorcontrib>LEI, Yi</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HU, Taocheng</au><au>YE, Qing</au><au>CAO, Yongqiang</au><au>LIU, Huili</au><au>LEI, Yi</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>SELF-LEARNING METHOD FOR SEMANTIC FEATURE WITH MAXIMUM GAP, AND COMPUTER DEVICE AND STORAGE MEDIUM</title><date>2024-06-26</date><risdate>2024</risdate><abstract>Disclosed in the present invention is a self-learning method for semantic features with the maximum gap. The method comprises: constructing an encoder, wherein a constraint condition for the encoder is that the distance between codes of two pieces of relevant data is less than the distance between codes of two pieces of irrelevant data; and driving, by using an unlabeled training set, the encoder to train by means of self-learning. Further disclosed in the present invention are a computer device and a computer-readable storage medium. By using the present invention, there is no need to label training data, and an encoder can be trained by directly using original data. In addition, a gap-based constraint condition is also introduced, such that the effect of a training model in practical applications can be effectively guaranteed.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_ZA202309602B
source esp@cenet
title SELF-LEARNING METHOD FOR SEMANTIC FEATURE WITH MAXIMUM GAP, AND COMPUTER DEVICE AND STORAGE MEDIUM
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T01%3A20%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=HU,%20Taocheng&rft.date=2024-06-26&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EZA202309602B%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true