Research map / Image generation / GANs
CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CartoonGAN instead of CycleGAN for unpaired photo-to-cartoon stylization | photo-to-cartoon stylization · Makoto Shinkai and Miyazaki Hayao styles | stylization quality | Helped |
| Use CartoonGAN instead of CNNMRF or Deep Analogy | photo-to-cartoon stylization · Paprika and Mamoru Hosoda styles | stylization quality | Helped |
| Use CartoonGAN instead of CycleGAN | unpaired photo-to-cartoon training · training, per epoch | training time: 2291.77s -> 1517.69s | Helped |
| Add the initialization phase (ablation) | CartoonGAN · Makoto Shinkai style | stylization quality | Helped |
| Use ℓ1 sparse regularization instead of ℓ2 for the VGG content loss (ablation) | CartoonGAN · Makoto Shinkai style | stylization quality | Helped |
| Add the edge-promoting adversarial loss (ablation) | CartoonGAN · Makoto Shinkai style | edge clarity | Helped |
Closest papers by meaning
- Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization (ICML 2022 · similarity 0.92)
- Learning to Cartoonize Using White-Box Cartoon Representations (CVPR 2020 · similarity 0.92)
- APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs (CVPR 2019 · similarity 0.89)
- Image-To-Image Translation With Conditional Adversarial Networks (CVPR 2017 · similarity 0.88)
- TraVeLGAN: Image-To-Image Translation by Transformation Vector Learning (CVPR 2019 · similarity 0.88)
- MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing (AAAI 2021 · similarity 0.87)
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