TY - GEN
T1 - Collabonet
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
AU - Lee, Hyeongmin
AU - Kim, Taeoh
AU - Song, Eungyeol
AU - Lee, Sangyoun
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - Designing models for learning dataset with complex distributions is one of the main challenges that still remains in machine learning areas. We propose CollaboNet, which can divide a large dataset into sub-datasets, train two generative models separately, and let two models work together to achieve better performance. The proposed algorithm divides a large dataset without label since the capability difference between two generative models in performing tasks on each data is the main criterion for dividing a large dataset. In other words, the classification model can be trained by unsupervised manner. Autoencoder experiments for pure MNIST and the datasets combined artificially from two image sets shows that CollaboNet successfully splits large datasets without labels, improving the performance of generative models.
AB - Designing models for learning dataset with complex distributions is one of the main challenges that still remains in machine learning areas. We propose CollaboNet, which can divide a large dataset into sub-datasets, train two generative models separately, and let two models work together to achieve better performance. The proposed algorithm divides a large dataset without label since the capability difference between two generative models in performing tasks on each data is the main criterion for dividing a large dataset. In other words, the classification model can be trained by unsupervised manner. Autoencoder experiments for pure MNIST and the datasets combined artificially from two image sets shows that CollaboNet successfully splits large datasets without labels, improving the performance of generative models.
UR - http://www.scopus.com/inward/record.url?scp=85062908117&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062908117&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2018.8451825
DO - 10.1109/ICIP.2018.8451825
M3 - Conference contribution
AN - SCOPUS:85062908117
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1068
EP - 1072
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
Y2 - 7 October 2018 through 10 October 2018
ER -