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Apprenticeship learning: Transfer of knowledge via dataset augmentation
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2013 (English)In: Image Analysis: 18th Scandinavian Conference, SCIA 2013, Espoo, Finland, June 17-20, 2013. Proceedings, Springer, 2013, 432-443 p.Conference paper, Published paper (Refereed)
Abstract [en]

In visual category recognition there is often a trade-off between fast and powerful classifiers. Complex models often have superior performance to simple ones but are computationally too expensive for many applications. At the same time the performance of simple classifiers is not necessarily limited only by their flexibility but also by the amount of labelled data available for training. We propose a semi-supervised wrapper algorithm named apprenticeship learning, which leverages the strength of slow but powerful classification methods to improve the performance of simpler methods. The powerful classifier parses a large pool of unlabelled data, labelling positive examples to extend the dataset of the simple classifier. We demonstrate apprenticeship learning and its effectiveness by performing experiments on the VOC2007 dataset - one experiment improving detection performance on VOC2007, and one domain adaptation experiment, where the VOC2007 classifier is adapted to a new dataset, collected using a GoPro camera.

Place, publisher, year, edition, pages
Springer, 2013. 432-443 p.
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 7944 LNCS
Keyword [en]
Apprenticeship learning, Category recognition, Classification methods, Detection performance, Domain adaptation, Positive examples, Semi-supervised, Transfer of knowledge, Apprentices, Experiments, Image analysis, Knowledge management, Classification (of information)
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:kth:diva-133252DOI: 10.1007/978-3-642-38886-6_41ISI: 000342988500041Scopus ID: 2-s2.0-84884472244ISBN: 978-364238885-9 (print)OAI: oai:DiVA.org:kth-133252DiVA: diva2:660319
Conference
18th Scandinavian Conference on Image Analysis, SCIA 2013; Espoo; Finland; 17 June 2013 through 20 June 2013
Note

QC 20131029

Available from: 2013-10-29 Created: 2013-10-29 Last updated: 2014-11-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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