TY - GEN
T1 - Semi-supervised Learning Using Generative Adversarial Networks
AU - Chang, Chuan Yu
AU - Chen, Tzu Yang
AU - Chung, Pau Choo
N1 - Funding Information:
α ligent Recognition Industry Ser ! " Areas Research Center Program within the frame-work of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Machine learning is a powerful tool in many applications, but the most difficult process in machine learning is the collection of data and the labeling of data. Unsupervised and semi-supervised learning has thus become an important issue. In this paper, we introduce a semi-supervised learning approach which using generative adversarial networks to generate training samples. Those imitated samples were involved in training set to train the classifier, this can improve the stability and robustness of the classifier models. To demonstrate the performance of the proposed framework, four benchmarks including Iris, MNIST, CIFAR-10, and SVHN datasets were evaluated. The experimental results show that even in a small amount of training data, the proposed framework can predict more accurately than the existing methods.
AB - Machine learning is a powerful tool in many applications, but the most difficult process in machine learning is the collection of data and the labeling of data. Unsupervised and semi-supervised learning has thus become an important issue. In this paper, we introduce a semi-supervised learning approach which using generative adversarial networks to generate training samples. Those imitated samples were involved in training set to train the classifier, this can improve the stability and robustness of the classifier models. To demonstrate the performance of the proposed framework, four benchmarks including Iris, MNIST, CIFAR-10, and SVHN datasets were evaluated. The experimental results show that even in a small amount of training data, the proposed framework can predict more accurately than the existing methods.
UR - http://www.scopus.com/inward/record.url?scp=85062774164&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062774164&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2018.8628663
DO - 10.1109/SSCI.2018.8628663
M3 - Conference contribution
AN - SCOPUS:85062774164
T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
SP - 892
EP - 896
BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
A2 - Sundaram, Suresh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Y2 - 18 November 2018 through 21 November 2018
ER -