| Citation: | WEI Hongxi, ZHANG Yue. Zero-shot image classification based on generative adversarial network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2345-2350. doi: 10.13700/j.bh.1001-5965.2019.0363(in Chinese)
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The problem of zero-shot image classification has become a research focus in the field of image classification. In this paper, a method based on generative adversarial network (GAN) is used to solve the problem of zero-shot image classification. By generating image features of unseen classes, the zero-shot classification task is transformed into a conventional image classification task. At the same time, this paper makes modifications to the discriminant network in the generative adversarial network to make the discriminating process more accurate. The experimental results show that the performance of the proposed method has been increased by 0.4%, 0.4% and 0.5% on the datasets of AWA, CUB and SUN, respectively. Therefore, the proposed method can generate the better features by improving the generative adversarial networks, which results in solving the problem of zero-shot image calssification effectively.
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