Research on Style Migration Techniques Based on Generative Adversarial Networks in Chinese Painting Creation
, e
24 mar 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 24 mar 2025
Ricevuto: 07 nov 2024
Accettato: 05 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0781
Parole chiave
© 2025 Ying Liu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Figure 1.

Figure 2.

Figure 3.

Figure 4.

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 | - | - |
