## 5 Experiments  
### Experimental Settings (Parameters)  
To implement this model, the Pytorch framework [39] was used. The training was performed with an NVIDIA GTX 2080 ti GPU. The networks were trained with the collected dataset. While training images with the dataset, we separated the training data by character as Datasets A, B, C, and D, so we could make the individual network for a particular character. We randomly flipped the images horizontally for training augmentation, as described on Section 3. Specific values of the parameter for model is shown on Table 1.  
For optimization, the ADAM [40] solver was implemented using the minibatch function. The initial learning rate was 0.0002 for both the generator and the discriminator. The total epoch time was 200, and the learning rate decayed slightly after 100 times, as shown in Algorithm 2. Approximately 20 min were required for training each particular dataset model. The shortest time was 19 min 41 s and the longest time was 20 min 22 s with the same amount of image data. ```
Types of Parameter & Value
``` Learning rate discriminator 0.0002 Learning rate generator 0.0002 Learning rate policy & linear Optimizer & Adam ```
Loaded image size & 512 \(\times\) 288  Epochs & 200  Epochs' decay & 100  Generator encoder layer conv & 64-128-256-256 (attention)-512-512-512-512-512 Generator decoder layer conv & 512-512-512-512-256-128-64 Discriminator layer conv & 64-128-256-512-512-512
```

**Algorithm 2**Optimizer learning rate algorithm. \begin{table}
\begin{tabular}{l l} \hline \hline
**Types of Parameter** & **Value** \\ \hline Learning rate discriminator & 0.0002 \\ Learning rate generator & 0.0002 \\ Learning rate policy & linear \\ Optimizer & Adam \\ Loaded image size & 512 \(\times\) 288  Epochs & 200 \\ Epochs’ decay & 100 \\ Generator encoder layer conv & 64-128-256-256 (attention)-512-512-512-512-512 Generator decoder layer conv & 512-512-512-512-256-128-64 Discriminator layer conv & 64-128-256-512-512-512 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of the parameters. ### Comparisons

Since the goal of our network is to colorize a particular character with few training data (10 images), there is no exact way to compare the result with previous colorization networks, which were trained with over 10,000 images with the desire to colorize random characters. Just for reference, we adopted Learned Perceptual Image Patch Similarity (LPIPS) [41] to make quantitative comparison results. LPIPS compares the generated image by the convolution feature extracting method rather than the color distribution matching method. This method can detect intra-class mode dropping and can measure diversity in the quality of the generated samples, which is a well-known proper comparison for GAN generated images. Even though the Frechet Inception Distance (FID) [42] is the best known method for generated image comparison, it requires at more than 5000 test images to have confident results, so it is not appropriate for our few shot line art image colorization experiment. In this experiment, we only cropped the image of the character except the background to make a fair comparison of the performance. As given in Table 2, the comparison of the LPIPS score shows a huge gap between Seg2 pix and the previous networks except for Pix2 pix (trained with the same dataset as Seg2 pix). Furthermore, we performed an ablation study by removing the attention layer and trap ball segment method from Seg2 pix (the same two sketch images were used as the input while removing the trap ball method). Since our Seg2 pix model was trained on a particular character to automate colorization, it obviously showed a good score compared to the previous colorization methods except Pix2 pix. However, Seg2 pix silk had a better score than Pix2 pix.