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Research on Style Migration Techniques Based on Generative Adversarial Networks in Chinese Painting Creation

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24 mar 2025

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Figure 1.

CycleGAN Model network internal structure
CycleGAN Model network internal structure

Figure 2.

Improved generator structure
Improved generator structure

Figure 3.

Student evaluations of the five algorithms
Student evaluations of the five algorithms

Figure 4.

Expert evaluation of the results of the four algorithms
Expert evaluation of the results of the four algorithms

Number of content samples in dataset

Object Train Set Test set Seen Unseen
Horse 1465 165
Shrimp 525 110
Cattle × 205
Dog × 835
Cat × 1115
Bird × 170
Tiger × 530
Lion × 445
Zebra × 110

Quantitative evaluation results of FID, SSIM and Kernel MMD

Gatys AdaIN CycleGAN CartoonGAN ChiGAN ICycleGAN
FID↓ 296.2014 224.5274 217.0988 252.3054 234.88873 180.0012
SSIM↑ 0.8719 0.9002 0.8107 0.8827 0.8791 0.9119
Kernel MMD↓ 1.085 1.0275 0.9846 1.1352 1.1045 0.9502

The details of the SWAN

Networks Operation Kernel size Stride Padding Feature maps Normalization Nonlinearity
Content Encoder Convolution 6 1 - 64 IN ReLU
Convolution 3 1 1 128 ReLU ReLU
Convolution 4 3 1 256 ReLU ReLU
Style Encoder Convolution 6 1 - 64 ReLU ReLU
Convolution 3 1 1 128 ReLU ReLU
Convolution 4 3 1 256 ReLU ReLU
Resnet Blocks Convolution 4 1 - 258 ReLU ReLU
Convolution 4 1 - 258 ReLU -
Decoder Convolution 4 3 1 128 ReLU ReLU
Convolution 4 3 1 64 ReLU ReLU
Convolution 6 2 - 3 - -