Semi-supervised image classification via attention mechanism and generative adversarial network

Xuezhi Xiang; Zeting Yu; Ning Lv; Xiangdong Kong; Abdulmotaleb El Saddik

doi:10.1117/12.2557747

3 January 2020 Semi-supervised image classification via attention mechanism and generative adversarial network

Xuezhi Xiang, Zeting Yu, Ning Lv, Xiangdong Kong, Abdulmotaleb El Saddik

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113731J (2020) https://doi.org/10.1117/12.2557747
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Image classification plays a vital role in the field of computer vision. Many existing image classification methods with high accuracy are based on supervised learning, which requires a great number of labeled images. However, the labeling of images requires a lot of human and material resources. In this paper, we focus on semi-supervised image classification, which can build a classifier using a few labeled images and plenty of unlabeled images. We propose an attention-based generative adversarial network (GAN) for semi-supervised image classification, which can capture global dependencies and adaptively extract important information. Furthermore, we apply spectral normalization, which can stabilize the training of attention-based GAN. The experimental results obtained with the CIFAR-10 dataset show that the proposed method is comparable with the state-of-the-art GAN-based semi-supervised image classification methods.

Citation Download Citation

Xuezhi Xiang, Zeting Yu, Ning Lv, Xiangdong Kong, and Abdulmotaleb El Saddik "Semi-supervised image classification via attention mechanism and generative adversarial network", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731J (3 January 2020); https://doi.org/10.1117/12.2557747

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available