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Discriminative regularization of the latent manifold of variational auto-encoders

Kossyk, Ingo and Marton, Zoltan-Csaba (2019) Discriminative regularization of the latent manifold of variational auto-encoders. Journal of Visual Communication and Image Representation, 61, pp. 121-129. Elsevier. doi: 10.1016/j.jvcir.2019.03.008. ISSN 1047-3203.

[img] PDF - Postprint version (accepted manuscript)

Official URL: http://dx.doi.org/10.1016/j.jvcir.2019.03.008


We present an approach on training classifiers or regressors using the latent embedding of variational auto-encoders (VAE), an unsupervised deep learning method, as features. Usually VAEs are trained using unlabeled data and independently from the classifier, whereas we investigate and analyze the performance of a classifier or regressor that is trained jointly with the variational deep network. We found that models trained this way can improve the embedding s.t. to increase classification performance, and also can be used for semi-supervised learning, building up the information extracting latent representation in an incremental fashion. The model was tested on two widely known computer vision benchmarks, and its generalization power was evaluated on an independent dataset. Additionally, generally applicable statistical methods are presented for evaluating similarly performing classifiers, and used to quantify the performance increase. The general applicability and ease-of-use of deep learning approaches allows for a wide applicability of the method.

Item URL in elib:https://elib.dlr.de/128125/
Document Type:Article
Title:Discriminative regularization of the latent manifold of variational auto-encoders
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Marton, Zoltan-CsabaUNSPECIFIEDhttps://orcid.org/0000-0002-3035-493XUNSPECIFIED
Date:May 2019
Journal or Publication Title:Journal of Visual Communication and Image Representation
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 121-129
Keywords:Variational auto-encoder Regularization Knowledge representation Perceptual data compaction Semi-supervised learning Statistical performance analysis
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung (old), R - Intuitive Human-Robot Interface [SY]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Institute of Robotics and Mechatronics (since 2013) > Cognitive Robotics
Deposited By: Marton, Dr. Zoltan-Csaba
Deposited On:28 Jun 2019 13:00
Last Modified:20 Jun 2021 15:52

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