Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi

Cagatay Isil; Berkan Solmaz; Aykut Koc

doi:10.1109/SIU.2018.8404227

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 proceeding › Conference 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.

Translated title of the contribution	Variational autoencoders with triplet loss for representation learning
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404227
State	Published - Jul 5 2018
Externally published	Yes
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: May 2 2018 → May 5 2018

Publication series

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

Conference

Conference
Country/Territory	Turkey
City	Izmir
Period	5/2/18 → 5/5/18

Keywords

Autoencoders
Deep learning
Representation learning
Triplet loss

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Signal Processing

Access to Document

10.1109/SIU.2018.8404227

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 proceeding › Conference 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

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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.",

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author = "Cagatay Isil and Berkan Solmaz and Aykut Koc",

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doi = "10.1109/SIU.2018.8404227",

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Üçlü Kayip Maliyetli Deǧisken Otokodlayicilar ile Temsil Öǧrenimi

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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