# [PERSON] Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis
## 3 Results and Discussion
### Experiments  
The primary objectives for the GANs in this paper are as follows: First, the generated patterns should appear inside the circle. Second, the generated patterns should be different between classes. Third, the generated patterns should not be identical to the training images. Further, the common settings of GANs are as follows: Batch normalization layers are excluded. The dimension of the input vector of the generator is 200, which follows a normal distribution, \(N(0,1)\). The training is finished at 300 epochs, and the results are derived in specific epochs: 0 for the beginning, 150 for the intermediate, and 300 for the last step. Figure 3 shows the graphical results of the GAN, and Figure 4 describes the architectures of the generators and discriminators of the GAN. It contains dense layers, activation layers using a rectified linear unit (ReLU), LeakyReLU, and hyperbolic tangent functions. It seems there were no improvements via training until at least 150 epochs. At 300 epochs, the graph shows that the GAN changes significantly. However, Figure 3g,h,i share almost identical outlines. That might indicate the mode collapse problem. The only difference is the level of green area. Therefore, the GAN seems inappropriate to generate synthetic radar images, because it does not satisfy the first and second objectives. Figure 5 describes the graphical results of the CGAN, and Figure 6 indicates the architectures of the generator and discriminator of the CGAN. It also contains dense layers, activation layers using a rectified linear unit (ReLU), LeakyReLU, and hyperbolic tangent function. The difference between it and the GAN is that class information is added when generating synthetic images. It seems that there are no meaningful improvements but noise in the whole epochs. Therefore, the CGAN also seems inadequate to generate synthetic radar images, because it does not satisfy the first and second objectives. Figure 7 indicates the graphical results of the DCGAN, and Figure 8 presents the architectures of the generator and discriminator of the DCGAN. Unlike previous models, the DCGAN includes not only dense layers and activation layers using a rectified linear unit (ReLU), LeakyReLU, and hyperbolic tangent function, but convolution layers that are appropriate to analyze and extract features of given images. It seems that there are remarkable changes compared to previous models. As the epoch increases, the generated pattern seems to build a circular shape. Unfortunately, the outside of the circle also has noisy data, as shown in Figure 7g,h,i. Therefore, the DCGAN also seems improper to generate synthetic radar images, because it does not satisfy the first and second objectives. But it provided us helpful information that includes the convolution layer in the architectures of the generator, and the discriminator allows obtaining favorable results. Figure 2: Original images of weather radar. Figure 4: Modules of GAN. Figure 3: Images generated by GAN. Figure 5: Images generated by CGAN. Figure 6: Modules of CGAN. Figure 8: Modules of DCGAN. Figure 7: Images generated by DCGAN. Figure 9 presents the graphical results of the InfoGAN, and Figure 10 shows the architectures of the generator and discriminator of the InfoGAN. The primary difference compared to the previous models, is that the discriminator has the Q network, which computes mutual information in the latent code \(c\) using the softmax layer. The other layers are the same as the DCGAN architecture: convolution layers, dense layers, and activation layers using the rectified linear unit (ReLU), LeakyReLU, and hyperbolic tangent function. In the early training stage, it was possible to expect positive results because the InfoGAN generated distinct and intense patterns, unlike previous models. However, as the learning progresses, the generated patterns seem not to converge to original images. It just generates exquisite patterns from given input vectors.