Attention-Based Generative Adversarial Network for Semi-supervised Image Classification

Abstract

Semi-supervised image classification is one of the areas of interest within the computer vision, which can build better classifiers using a few labeled images and plenty of unlabeled images. Recently, semi-supervised image classification methods based on the generative adversarial network (GAN) get promising results. In this paper, we introduce a self-attention mechanism to propose an attention-based 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. We also adopt manifold regularization as an additional regularization term so that we can make the most of the unlabeled images. We test the proposed method on SVHN and CIFAR-10 datasets. The experimental results show that the proposed method is comparable with the state-of-the-art GAN-based semi-supervised image classification methods.