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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CPUQ2
Repositorysid.inpe.br/sibgrapi/2021/09.05.19.30
Last Update2021:09.05.19.30.23 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.05.19.30.23
Metadata Last Update2022:06.14.00.00.26 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00031
Citation KeySilvaPedFarPapAlm:2021:ImTrDo
TitleImproving Transferability of Domain Adaptation Networks Through Domain Alignment Layers
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size1571 KiB
2. Context
Author1 Silva, Lucas Fernando Alvarenga e
2 Pedronette, Daniel Carlos Guimarães
3 Faria, Fabio Augusto
4 Papa, João Paulo
5 Almeida, Jurandy
Affiliation1 Universidade Federal de São Paulo 
2 São Paulo State University 
3 Universidade Federal de São Paulo 
4 São Paulo State University 
5 Universidade Federal de São Paulo
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 Addresse.lucas@unifesp.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 19:30:23 :: e.lucas@unifesp.br -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:38:00 :: 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
Keywordsdeep learning
unsupervised domain adaptation
image recognition
AbstractDeep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks. However, on real-world scenarios with partially or no labeled data, DL methods are also prone to the well-known domain shift problem. Multi-source unsupervised domain adaptation (MSDA) aims at learning a predictor for an unlabeled domain by assigning weak knowledge from a bag of source models. However, most works conduct domain adaptation leveraging only the extracted features and reducing their domain shift from the perspective of loss function designs. In this paper, we argue that it is not sufficient to handle domain shift only based on domain-level features, but it is also essential to align such information on the feature space. Unlike previous works, we focus on the network design and propose to embed Multi-Source version of DomaIn Alignment Layers (MS-DIAL) at different levels of the predictor. These layers are designed to match the feature distributions between different domains and can be easily applied to various MSDA methods. To show the robustness of our approach, we conducted an extensive experimental evaluation considering two challenging scenarios: digit recognition and object classification. The experimental results indicated that our approach can improve state-of-the-art MSDA methods, yielding relative gains of up to +30.64% on their classification accuracies.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Improving Transferability of...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Improving Transferability of...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CPUQ2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CPUQ2
Languageen
Target Filesibgrapi95.pdf
User Groupe.lucas@unifesp.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 4
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
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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