北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (12): 2345-2350.doi: 10.13700/j.bh.1001-5965.2019.0363

• 论文 • 上一篇    下一篇

基于生成对抗网络的零样本图像分类

魏宏喜, 张越   

  1. 内蒙古大学 计算机学院, 呼和浩特 010021
  • 收稿日期:2019-07-08 出版日期:2019-12-20 发布日期:2019-12-31
  • 通讯作者: 魏宏喜 E-mail:cswhx@imu.edu.cn
  • 作者简介:魏宏喜 男,博士,教授,博士生导师。主要研究方向:机器学习与数据挖掘;张越 男,硕士研究生。主要研究方向:图像分类。
  • 基金资助:
    国家自然科学基金(61463038);内蒙古自治区自然科学基金重大项目(2019ZD14)

Zero-shot image classification based on generative adversarial network

WEI Hongxi, ZHANG Yue   

  1. College of Computer Science, Inner Mongolia University, Hohhot 010021, China
  • Received:2019-07-08 Online:2019-12-20 Published:2019-12-31
  • Supported by:
    National Natural Science Foundation of China (61463038); Natural Science Foundation of Inner Mongolia Autonomous Region (2019ZD14)

摘要: 在图像分类任务中,零样本图像分类问题已成为一个研究热点。为了解决零样本图像分类问题,采用一种基于生成对抗网络(GAN)的方法,通过生成未知类的图像特征使得零样本分类任务转换为传统的图像分类任务。同时对生成对抗网络中的判别网络做出改进,使其判别过程更加准确,从而进一步提高生成图像特征的质量。实验结果表明:所提方法在AWA、CUB和SUN数据集上的分类准确率分别提高了0.4%、0.4%和0.5%。因此,所提方法通过改进生成对抗网络,能够生成质量更好的图像特征,从而有效解决零样本图像分类问题。

关键词: 深度学习, 图像分类, 零样本学习, 生成对抗网络(GAN), 图像特征生成

Abstract: 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.

Key words: deep learning, image classification, zero-shot learning, generative adversarial network (GAN), image feature generation

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