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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RRA45S
Repositorysid.inpe.br/sibgrapi/2018/09.16.01.52
Last Update2018:09.16.01.52.35 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/09.16.01.52.35
Metadata Last Update2022:06.14.00.09.29 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00055
Citation KeyNazareCostMellPont:2018:EmAnUs
TitleColor quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks
FormatOn-line
Year2018
Access Date2024, May 01
Number of Files1
Size395 KiB
2. Context
Author1 Nazare, Tiago S.
2 Costa, Gabriel B. Paranhos da
3 Mello, Rodrigo F. de
4 Ponti, Moacir A.
Affiliation1 University of São Paulo
2 University of São Paulo
3 University of São Paulo
4 University of São Paulo
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addresstiagosn@usp.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-09-16 01:52:35 :: tiagosn@usp.br -> administrator ::
2022-06-14 00:09:29 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDeep learning
transfer learning
convolutional neural networks
computer vision
AbstractTransfer learning is seen as one of the most promising areas of machine learning. Lately, features from pre-trained models have been used to achieve state-of-the-art results in several machine vision problems. Those models are usually employed when the problem of interest does not have enough supervised examples to support the network training from scratch. Most applications use networks pre-trained on noise-free RGB image datasets, what is observed even when the target domain counts on grayscale images or when data is degraded by noise. In this paper, we evaluate the use of Convolutional Neural Networks (CNNs) on such transfer learning scenarios and the impact of using RGB trained networks on grayscale image tasks. Our results confirm that the use of networks trained using colored images on grayscale tasks hinders the overall performance when compared to a similar network trained on a quantized version of the original dataset. Results also show that higher quantization levels (resulting in less colors) increase the robustness of CNN features in the presence of noise.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Color quantization in...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Color quantization in...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RRA45S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RRA45S
Languageen
Target FileSIB_2018.pdf
User Grouptiagosn@usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 7
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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