Data enhancement method based on cyclic generation adversarial network


  • Shiling Li
  • Tianhong Wang



GAN, Data Augmentation, Object detection, CycleGAN


This study introduces a novel data augmentation technique employing Cycle Generative Adversarial Networks (CycleGAN) to mitigate the challenges posed by the paucity of image datasets in deep learning domains. Through the adept training of a CycleGAN model, this method substantially enriches image datasets, thereby enhancing the efficiency of deep learning models in target detection tasks. Distinct from conventional approaches, our strategy incorporates advanced activation functions, relative discriminators, and residual connections, which collectively foster greater image diversity and mitigate mode collapse, all while maintaining a low computational overhead. Evaluations conducted on diverse datasets, including MNIST, Synthetic Aperture Radar (SAR), and medical blood cell images, demonstrate the method's superior augmentation capabilities compared to traditional Deep Convolutional GAN (DCGAN) techniques, underscoring its efficacy and potential utility in preprocessing for deep learning applications.


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How to Cite

Li, S., & Wang, T. (2024). Data enhancement method based on cyclic generation adversarial network. Journal of Computing and Electronic Information Management, 12(2), 70-75.

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