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 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 | |
| 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 | Turkey |
| City | Izmir |
| Period | 5/2/18 → 5/5/18 |
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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
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
}
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 -