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3 January 2020 Semi-supervised image classification via attention mechanism and generative adversarial network
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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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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