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
Publication statusPublished - 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

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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