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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CQ8ML
Repositorysid.inpe.br/sibgrapi/2021/09.05.20.54
Last Update2021:09.05.20.54.04 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.05.20.54.04
Metadata Last Update2022:06.14.00.00.26 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00019
Citation KeyLopesJrSchw:2021:AnEfDi
TitleAnalyzing the Effects of Dimensionality Reduction for Unsupervised Domain Adaptation
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size3683 KiB
2. Context
Author1 Lopes Junior, Renato Sergio
2 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais 
2 Universidade Federal de Minas Gerais
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressrenato.junior@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-05 20:54:04 :: renato.junior@dcc.ufmg.br -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:27:20 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:26 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordscomputer vision
machine learning
domain adaptation
transfer learning
AbstractDeep neural networks are extensively used for solving a variety of computer vision problems. However, in order for these networks to obtain good results, a large amount of data is necessary for training. In image classification, this training data consists of images and labels that indicate the class portrayed by each image. Obtaining this large labeled dataset is very time and resource consuming. Therefore, domain adaptation methods allow different, but semantic-related, datasets that are already labeled to be used during training, thus eliminating the labeling cost. In this work, the effects of embedding dimensionality reduction in a state-of-the-art domain adaptation method are analyzed. Furthermore, we experiment with a different approach that use the available data from all domains to compute the confidence of pseudo-labeled samples. We show through experiments in commonly used datasets that, in fact, the proposed modifications led to better results in the target domain in some scenarios.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Analyzing the Effects...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Analyzing the Effects...
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CQ8ML
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CQ8ML
Languageen
Target File78_final.pdf
User Grouprenato.junior@dcc.ufmg.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 3
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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