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.
T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Y2 - 2 May 2018 through 5 May 2018
ER -