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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RNND3S
Repositorysid.inpe.br/sibgrapi/2018/08.31.10.25
Last Update2018:08.31.10.25.31 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.31.10.25.31
Metadata Last Update2022:06.14.00.09.15 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00056
Citation KeyPeixinhoBenaNonaFalc:2018:DeTrDa
TitleDelaunay Triangulation Data Augmentation guided by Visual Analytics for Deep Learning
FormatOn-line
Year2018
Access Date2024, May 19
Number of Files1
Size2931 KiB
2. Context
Author1 Peixinho, Alan Zanoni
2 Benato, Bárbara Caroline
3 Nonato, Luis Gustavo
4 Falcão, Alexandre Xavier
Affiliation1 University of Campinas
2 University of Campinas
3 University of São Paulo
4 University of Campinas
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 Addressbarbarabenato@gmail.com
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)2018-08-31 10:25:31 :: barbarabenato@gmail.com -> administrator ::
2022-06-14 00:09:15 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDelaunay Triangulation
Data Augmentation
Visual Analytics
Deep Learning
Encoder-Decoder Neural Network
Convolutional Neural Network
AbstractIt is well known that image classification problems can be effectively solved by Convolutional Neural Networks (CNNs). However, the number of supervised training examples from all categories must be high enough to avoid model over- fitting. In this case, two key alternatives are usually presented (a) the generation of artificial examples, known as data aug- mentation, and (b) reusing a CNN previously trained over a large supervised training set from another image classification problem a strategy known as transfer learning. Deep learning approaches have rarely exploited the superior ability of humans for cognitive tasks during the machine learning loop. We advocate that the expert intervention through visual analytics can improve machine learning. In this work, we demonstrate this claim by proposing a data augmentation framework based on Encoder- Decoder Neural Networks (EDNNs) and visual analytics for the design of more effective CNN-based image classifiers. An EDNN is initially trained such that its encoder extracts a feature vector from each training image. These samples are projected from the encoder feature space on to a 2D coordinate space. The expert includes points to the projection space and the feature vectors of the new samples are obtained on the original feature space by interpolation. The decoder generates artificial images from the feature vectors of the new samples and the augmented training set is used to improve the CNN-based classifier. We evaluate methods for the proposed framework and demonstrate its advantages using data from a real problem as case study the diagnosis of helminth eggs in humans. We also show that transfer learning and data augmentation by affine transformations can further improve the results.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RNND3S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RNND3S
Languageen
Target FilePID5546301.pdf
User Groupbarbarabenato@gmail.com
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 12
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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