A method for improving CNN-based image recognition using DCGAN

Wei Fang, Feihong Zhang, Victor S. Sheng, Yewen Ding

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


Image recognition has always been a hot research topic in the scientific community and industry. The emergence of convolutional neural networks(CNN) has made this technology turned into research focus on the field of computer vision, especially in image recognition. But it makes the recognition result largely dependent on the number and quality of training samples. Recently, DCGAN has become a frontier method for generating images, sounds, and videos. In this paper, DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model. We combine DCGAN with CNN for the second time. Use DCGAN to generate samples and training in image recognition model, which based by CNN. This method can enhance the classification model and effectively improve the accuracy of image recognition. In the experiment, we used the radar profile as dataset for 4 categories and achieved satisfactory classification performance. This paper applies image recognition technology to the meteorological field.

Original languageEnglish
Pages (from-to)167-178
Number of pages12
JournalComputers, Materials and Continua
Issue number1
StatePublished - 2018


  • CNN
  • Image recognition
  • Samples.


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