Wang, Tick Son und Marton, Zoltan-Csaba und Brucker, Manuel und Triebel, Rudolph (2017) How Robots Learn to Classify New Objects Trained from Small Data Sets. 1st Conference on Robot Learning, 2017-11-13 - 2017-11-15, Mountain View, United States.
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Kurzfassung
In this paper, we address the problem of learning to classify new object classes and instances by adapting a previously trained classifier. The main challenges here are the small amount of newly available training data and the large change in appearance between the new and the old data. To address this we propose a new variant of Progressive Neural Networks (PNN), originally introduced by Rusu et al. [1]. We show that by performing a specific simplification in the adapters, the prediction performance of the resulting PNN can be significantly increased. Furthermore, we give additional insights about when PNNs outperform alternative methods, and provide empirical evaluations on benchmark datasets. Finally, we also suggests a way of using it to augment the functionality of a network by extending it with new classes, addressing the problem of unbalanced classes, i.e. where the new classes are under-represented.
elib-URL des Eintrags: | https://elib.dlr.de/116840/ | ||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
Titel: | How Robots Learn to Classify New Objects Trained from Small Data Sets | ||||||||||||||||||||
Autoren: |
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Datum: | 2017 | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | Progressive Neural Network, Robotic Vision, Transfer Learning | ||||||||||||||||||||
Veranstaltungstitel: | 1st Conference on Robot Learning | ||||||||||||||||||||
Veranstaltungsort: | Mountain View, United States | ||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
Veranstaltungsbeginn: | 13 November 2017 | ||||||||||||||||||||
Veranstaltungsende: | 15 November 2017 | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||
HGF - Programmthema: | Technik für Raumfahrtsysteme | ||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | R SY - Technik für Raumfahrtsysteme | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben Multisensorielle Weltmodellierung (alt) | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition | ||||||||||||||||||||
Hinterlegt von: | Brucker, Manuel | ||||||||||||||||||||
Hinterlegt am: | 08 Dez 2017 16:51 | ||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:21 |
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