Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi

Translated title of the contribution: Variational autoencoders with triplet loss for representation learning

Cagatay Isil, Berkan Solmaz, Aykut Koc

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Learning low dimensional meaningful representations of data is an important task for classification, visualization and compression. Using autoencoders for representation learning is a successful application of deep learning. Recently, variational autoencoders have also been developed. These are more advantageous than autoencoders since these are generative and have a compact form in the latent space. In order to improve the clustering performance of variational autoencoders in the latent space, the use of variational autoencoders with triplet loss is proposed in this study.

Original languageTurkish
Title of host publication26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538615010
DOIs
StatePublished - Jul 5 2018
Externally publishedYes
Event26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey
Duration: May 2 2018May 5 2018

Publication series

Name26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference
CountryTurkey
CityIzmir
Period5/2/185/5/18

Fingerprint

Visualization
Deep learning

Keywords

  • Autoencoders
  • Deep learning
  • Representation learning
  • Triplet loss

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing

Cite this

Isil, C., Solmaz, B., & Koc, A. (2018). Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404227

Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi. / Isil, Cagatay; Solmaz, Berkan; Koc, Aykut.

26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Isil, C, Solmaz, B & Koc, A 2018, Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 5/2/18. https://doi.org/10.1109/SIU.2018.8404227
Isil C, Solmaz B, Koc A. Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4. (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). https://doi.org/10.1109/SIU.2018.8404227
Isil, Cagatay ; Solmaz, Berkan ; Koc, Aykut. / Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).
@inproceedings{f69fd9f130e740f29dfbbe8d72263abf,
title = "{\"U}{\cc}l{\"u} Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil {\"O}ǧrenimi",
abstract = "Learning low dimensional meaningful representations of data is an important task for classification, visualization and compression. Using autoencoders for representation learning is a successful application of deep learning. Recently, variational autoencoders have also been developed. These are more advantageous than autoencoders since these are generative and have a compact form in the latent space. In order to improve the clustering performance of variational autoencoders in the latent space, the use of variational autoencoders with triplet loss is proposed in this study.",
keywords = "Autoencoders, Deep learning, Representation learning, Triplet loss",
author = "Cagatay Isil and Berkan Solmaz and Aykut Koc",
year = "2018",
month = "7",
day = "5",
doi = "10.1109/SIU.2018.8404227",
language = "Turkish",
series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

}

TY - GEN

T1 - Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi

AU - Isil, Cagatay

AU - Solmaz, Berkan

AU - Koc, Aykut

PY - 2018/7/5

Y1 - 2018/7/5

N2 - Learning low dimensional meaningful representations of data is an important task for classification, visualization and compression. Using autoencoders for representation learning is a successful application of deep learning. Recently, variational autoencoders have also been developed. These are more advantageous than autoencoders since these are generative and have a compact form in the latent space. In order to improve the clustering performance of variational autoencoders in the latent space, the use of variational autoencoders with triplet loss is proposed in this study.

AB - Learning low dimensional meaningful representations of data is an important task for classification, visualization and compression. Using autoencoders for representation learning is a successful application of deep learning. Recently, variational autoencoders have also been developed. These are more advantageous than autoencoders since these are generative and have a compact form in the latent space. In order to improve the clustering performance of variational autoencoders in the latent space, the use of variational autoencoders with triplet loss is proposed in this study.

KW - Autoencoders

KW - Deep learning

KW - Representation learning

KW - Triplet loss

UR - http://www.scopus.com/inward/record.url?scp=85050825414&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050825414&partnerID=8YFLogxK

U2 - 10.1109/SIU.2018.8404227

DO - 10.1109/SIU.2018.8404227

M3 - Conference contribution

AN - SCOPUS:85050825414

T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

SP - 1

EP - 4

BT - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -