Data Generation Based on Generative Adversarial Network with Spatial Features

SUN Lei; YANG Yu; MAO Xiuqing; WANG Xiaoqin; LI Jiaxin

doi:10.11999/JEIT211285

Volume 45 Issue 6

Jun. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(6): 1959-1969

SUN Lei, YANG Yu, MAO Xiuqing, WANG Xiaoqin, LI Jiaxin. Data Generation Based on Generative Adversarial Network with Spatial Features[J]. Journal of Electronics & Information Technology, 2023, 45(6): 1959-1969. doi: 10.11999/JEIT211285

Citation:

SUN Lei, YANG Yu, MAO Xiuqing, WANG Xiaoqin, LI Jiaxin. Data Generation Based on Generative Adversarial Network with Spatial Features[J]. Journal of Electronics & Information Technology, 2023, 45(6): 1959-1969. doi: 10.11999/JEIT211285

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Data Generation Based on Generative Adversarial Network with Spatial Features

doi: 10.11999/JEIT211285

PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China

Received Date: 2021-11-17
Accepted Date: 2022-01-20
Rev Recd Date: 2022-01-10

Available Online: 2022-02-03

Publish Date: 2023-06-10

Abstract

Abstract

Traditional Generative Adversarial Network (GAN) ignores the representation and structural information of the original feature when the feature map is large, and there is no remote correlation between the pixels of the generated images, resulting image quality is low. To improve the quality of the generated images further, a method of data generation based on Generative Adversarial Network with Spatial Features (SF-GAN) is proposed. Firstly, the spatial pyramid network is added into the generator and discriminator to capture the important description information better such as the edge of the images. Then the features of the generator and discriminator are strengthened to model the remote correlation between pixels. Experiments are performed with small-scale benchmarks (CelebA, SVHN, and CIFAR-10). Compared with improved training of Wasserstein GANs (WGAN-GP) and Self-Attention Generative Adversarial Networks (SAGAN) by qualitative and quantitative evaluation of Inception Score (IS) and Frechet Inception Distance (FID), the proposed method can generate higher quality images. The experiment proves that the generated images can improve the training effect of the classified model further.
- Generative Adversarial Network (GAN),
- Spatial pyramid network,
- Feature strengthen,
- Feature maps

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References(29)

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