# Generating Scenery Images with Larger Variety According to User Descriptions
## 3 Experimental Results
Figure 4 compares the FID values of the proposed method and AttnGAN using a different number of training epochs. We can observe that IS and FID do not improve much after number of epochs is larger than 50. Therefore, we select 50 as the number of epochs in the rest of the experiments to save training time. From Figures 3 and 4, we can see that the proposed method can achieve lower FID while maintaining similar or slightly higher IS compared to AttnGAN. Figure 5 displays selected scenery images generated using training descriptions. Images in Figure 5 are generated using the description \"forest in the morning\". Figure 5b-d display selected images generated using testing description combinations. Images in Figure 5b are generated using the description \"hayfield in autumn morning.\" Images in Figure 5c are generated using the description \"beach in winter morning,\" and those in Figure 5d are generated using the description \"sand in autumn.\" We can observe that, although images generated under the training  
descriptions look more realistic, images generated under testing description combinations still exhibit decent quality. This indicates that the model has learned from the word vectors to generate season, time and place combinations that it has never seen in the training images. The limitation of this work is similar to existing GAN-based methods. The model can generate good results if the input is mapped into the learned subspace. However, data that is unseen would be mapped out of the subspace. For example, since a baseball field is not in the training dataset, text descriptions containing baseball fields could not be generated properly. Figure 4: Comparisons on Fréchet Inception Distance. Figure 3: Comparisons on Inception Score. Figure 6 compares some images generated by the proposed method and AttnGAN. We use the same description to generate 12 images at a time using both methods to observe the differences. Figure 6a,b shows images generated under the description \"canal at night.\" Figure 6c,d displays images generated under the description \"canal in autumn.\" Images in Figure 6a,c are generated by AttnGAN. Additionally, images in Figure 6b,d are generated by the proposed method. We can see that similar images appear repeatedly in Figure 6a,c, and the images in Figure 6b,d exhibit higher diversity. From Figure 6a, we can see that the results have similar colors. Additionally, the buildings along the canal are very much alike. We can observe that the images in Figure 6b contain different types of buildings, terrains, and reflection patterns. It is clear that the proposed imagination model is able to generate images with larger variety. Although the generated image in the top right corner of Figure 6b is an unsuccessful case, such failed examples also happen in Figure 6c (image on row 3, column 2 and column 3) generated by AttnGAN. Both methods inevitably generate unsatisfying results occasionally. Figure 5: Images Generated by Different Description Combinations Using the Proposed Method. (**a**) “forest in the morning”, (**b**) “hybrid in autumn morning”, (**c**) “beach in winter morning”, (**d**) “sand in autumn”. For subjective qualitative evaluation, twenty users are invited to evaluate the results generated by the proposed method and AttnGAN. Each user is asked to evaluate the generated images using 30 different descriptions. For each description, twelve images are generated at a time so that the user can evaluate the variety of the generated images under the same description. Each user performs evaluation by choosing which result he or she prefers in terms of quality and variety. Among all user scores, 53% of them prefer the proposed method in terms of image quality. Additionally, 79% of the user scores prefer the proposed method in terms of variety. This subjective evaluation shows that the proposed method has both superior quality and more variety. One possible application for the proposed framework is data augmentation for autonomous vehicles.