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Incremental Deep Learning for Object Classification

Wang, Tick Son (2017) Incremental Deep Learning for Object Classification. Master's. DLR-Interner Bericht. DLR-IB-RM-OP-2017-132.

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Abstract

In robotics applications, it is common for tasks to be modified over time. For example, a classification task might be expanded over time to classify more and more classes. The focus of this thesis is to compare the potential of the traditional fine-tuning approach and the newly proposed Progressive Neural Network (PNN) approach [Rusu et al., 2016a] to incrementally adapt a deep predictive model to such dynamically changing tasks. Empirical results are presented in thesis showing that in certain scenarios PNN is significantly more effective than fine-tuning in this regard. To determine these prospective scenarios, where PNN is expected to outperform the fine-tuning approach, this thesis presented a hypothesis which is validated by the experiment results. In addition to that, this thesis also proposed a new method to extend a classifier with new classes with PNN. The experiment results in this thesis showed that it is more effective and reliable than the fine-tuning approach.

Item URL in elib:https://elib.dlr.de/117632/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Incremental Deep Learning for Object Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wang, Tick SonTick.Wang (at) dlr.deUNSPECIFIED
Date:15 July 2017
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Progressive Neural Network, Deep Learning, Robot Vision, Computer Vision
Institution:Technische Universität München
Department:Department of Informatics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Brucker, Manuel
Deposited On:21 Dec 2017 10:10
Last Modified:21 Dec 2017 10:10

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