# TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks
## 4 Result and Discussion  
### Quantitative Evaluation with a Bird Image Dataset  
In the evaluation of the proposed model, the Caltech-UCSD Birds-200 (CUB) [40] dataset was employed. The CUB dataset consists of 11,788 images for 200 different types of birds. Each image is annotated with attributes of birds, which correspond to the colors and shapes of the birds. By using this annotation, text captions to describe the images have been employed in GAN-INT-CLS, where 27,450 pairs of images and corresponding texts exist; whereas there are 11,788 images in the dataset, these pairs are composed of multiple texts for particular images. The text encoding vector y has a dimension of 1024 for each text. In this experiment, images with a 64 \(\times\) 64 resolution were produced and evaluated; thus, real images in the dataset were down-scaled with the same size in the preprocessing step.  
For the quantitative evaluation metrics, Inception score (IS) [39] and Frechet Inception distance (FID) [41] were used, where these metrics are conventional methods to evaluate generative models. These metrics commonly use a pre-trained Inception network, and specific layer outputs of the network are employed to assess the quality of generated images; a high IS and a low FID indicate superior performance. The IS was measured with ten different sets of 5000 samples, which is a conventional method for calculating IS; then, the average IS and standard deviation were computed.  
In the evaluation, two baseline models were compared to the proposed model. First, cGAN-based GAN-INT-CLS was evaluated with IS and FID in order to demonstrate the performance of the DA methods integrated with the independent regressor. Additionally, TextControlGAN without DA was assessed to verify that the independent regressor cannot solely perform without DA methods.  
Table 1 showcases the performance of various models, including recent models, such as AttnGAN and DM-GAN [53], highlighting that the proposed TextControlGAN outperforms other methods, including GAN-INT-CLS. TextControlGAN achieved an IS of 4.41 \(\pm\) 0.02, signifying a 17.6% improvement compared to GAN-INT-CLS in terms of IS. This demonstrates the effectiveness of the proposed framework, which employs an independent regressor in conjunction with DA techniques, in accurately learning conditional text. Moreover, the FID for TextControlGAN decreased by 36.6% relative to GAN-INT-CLS, further substantiating TextControlGAN's superior performance.  
However, when DA techniques were excluded from the proposed architecture (TextControlGAN w/o DA), the performance declined compared to that of TextControlGAN. This outcome is believed to stem from overfitting issues in the regressor, as DA techniques generally reduce overfitting in neural network models. As a result, this finding highlights the necessity of incorporating DA techniques when training the proposed architecture to ensure optimal performance.  
An evaluation of the proposed model, TextControlGAN, was conducted to assess its computing time per epoch, in comparison to GAN-INT-CLS. The results indicated that the proposed model required approximately 447.56 s, while GAN-INT-CLS necessitated approximately 637.55 s per epoch. Despite being 29.8% less computationally efficient than GAN-INT-CLS, TextControlGAN surpassed GAN-INT-CLS in terms of performance, as evidenced by the presented results.