# Generating Scenery Images with Larger Variety According to User Descriptions
## 3 Experimental Results  
The experimental results are presented in this section. To train the proposed framework, the training dataset is collected from Open Images Dataset V4 [34] and Places [35]. Open Images Dataset V4 contains 19,995 machine-labeled classes. Each label is provided with a confidence score. We use these labels to select the desired training images for the proposed system. Additionally, images with desired labels are selected from the Places dataset automatically. We select 26 different training descriptions for the Open Images Dataset V4, and 37 different training texts for the Places dataset in the experiments. We refer to the collected dataset set as the Scene dataset. In the Scene dataset, there are total 63 different descriptions. Each description contains 685 images. The total image count is 43,155. To test the proposed system, 1200 images are generated for each training description. Additionally, additional testing descriptions are established by combining different place descriptions randomly with some season or time descriptions in the training texts. There are a total of 76 additional testing description combinations. We also generate 1200 images for each additional testing description. Consequently, 166,800 images are generated to evaluate the image generation system in total. The experiment environment and settings are listed in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Hardware**} \\ \hline CPU & i7-8700k \\ RAM & 64 GB \\ GPU & nvidia 1080 ti \\ \hline \multicolumn{2}{c}{**Environment**} \\ \hline OS & Ubuntu 16.04.5 LTS \\ Docker & 18.06.1-ce \\ Python & python3.5.2 \\ IDE & Vscodel1.34.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiment Environment and Settings. Two metrics, Inception Score (IS) [36] and Frechet Inception Distance (FID) [37], are used to evaluate the generated images in the experiments. The Inception Score uses an Inception v3 network pretrained on ImageNet. It calculates the statistics of the outputs of the Inception v3 network when applied to generated images. The definition of IS is expressed in Equation (14), where \(x\sim p_{g}\) denotes that \(x\) is an image sampled from the trained generative model. \[IS(G)=exp\Big{(}\mathbb{E}_{x\sim p_{g}}D_{KL}(p(y|x)\|p(y))\Big{)} \tag{14}\]  
\[D_{KL}(P\|Q)=\sum_{i}P(i)log\frac{P(i)}{Q(i)} \tag{15}\]  
\(D_{KL}(P\|Q)\) is the KL divergence between the distributions \(p\) and \(q\). In Equation (14), \(p(y|x)\) is the conditional class distribution, and \(p(y)\) is the marginal class distribution. Higher inception scores indicate that the generated images have larger variety, and each image can be distinctly classified as some category. Therefore, the higher the inception score, the better the generative model is. As indicated in [36], there are some drawbacks to use inception score alone. Frechet Inception Distance is proposed to remedy the issues of IS. To calculate FID, the inception network is used to extract features from an intermediate layer. The definition of FID between real data \(\mathbb{P}_{r}\) and generated data \(\mathbb{P}_{g}\) is expressed in Equation (16). \[\text{FID}\big{(}\mathbb{P}_{r},\mathbb{P}_{g}\big{)}=\left\|\mu_{r}-\mu_{g} \right\|^{2}+\textit{Tr}\bigg{(}C_{r}+C_{g}-2\big{(}C_{r}C_{g}\big{)}^{\frac{ 1}{2}}\bigg{)} \tag{16}\]  
In Equation (16), \(\mu_{r}\) and \(\mu_{g}\) denote the mean of the real data and generated data, respectively. Additionally, \(C_{r}\) and \(C_{g}\) denote the covariance matrices of the real data and generated data, respectively. Finally, \(\textit{Tr}\) sums up all the diagonal elements. Lower FID values indicate better quality and diversity for generated images. FID is more robust to noise than IS, and it is also a better measurement for image diversity. Figure 3 shows the inception scores of the proposed method and AttnGAN [30] using a different number of training epochs. The number of epochs ranges from 10 to 65 in the training process.