[1]GUAN Fengxu,LU Siqi,ZHENG Yan.Dual discriminator residual generation adversarial network with multiscale feature fusion[J].CAAI Transactions on Intelligent Systems,2023,18(5):917-925.[doi:10.11992/tis.202207005]
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Dual discriminator residual generation adversarial network with multiscale feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 917-925 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
Dual discriminator residual generation adversarial network with multiscale feature fusion
Author(s):
GUAN Fengxu;  LU Siqi;  ZHENG Yan
School of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
generative adversarial networks; deep learning; unsupervised model; mode collapse; gradient explosion; gradient disappearance; multiscale feature fusion; training stability
CLC:
TP18
DOI:
10.11992/tis.202207005
Abstract:
Generative adversarial networks (GANs), a class of unsupervised generative models based on deep learning, have shown promising results in generating realistic and high-quality images without modeling the data distribution. However, standard GANs are often difficult to train and often suffer from gradient disappearance, gradient explosion, or mode collapse, which can limit the overall performance of the model. To address the mode collapse problem and improve the variety of the generated images, this paper proposes a dual discriminator structure. Furthermore, this paper improves the generator and discriminator model and proposes a generator based on residual network and multiscale feature fusion and a discriminator based on multiscale feature fusion, which effectively solves the problem of gradient disappearance and gradient explosion that occurs in deep networks while improving the quality of generated images. The proposed approach is applied to various datasets, including MNIST, LSUN, and CelebA. The training results reveal that the stability and quality of the generated images are high, achieving satisfying FID and IS values.
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