## 6 Experiments
### Quality of Generated Image under Basic Architectures  
In this section, we conduct experiments on real-world large-scale datasets [30,31] and present both qualitative and quantitative evaluation results. To ensure a fair comparison, we adopt experimental settings that are identical to those used in previous works [14,18] under the same basic architecture configurations. As a result, we list the results from the unconditional unsupervised GAN-based models and compare them with our SoftGAN. Similar to WGAN-GP [14] and CT-GAN [18], we utilize the CIFAR-10 [30] dataset to evaluate two architecture configurations for the generative model: one is a small CNN architecture, and the other is a ResNet architecture. Tables 2 and 3 show the two architectures, respectively. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Generator** & **Output Shape** & **Discriminator** & **Output Shape** \\ \hline Noise & [32, 128] & Image & [32, 3, 32, 32] \\ MLP\(\rightarrow\)ReLU\(\rightarrow\)BN\(\rightarrow\)Reshape & [32, 512, 4, 4] & 5 \(\times\) 5 Conv\(\rightarrow\)IReLU & [32, 128, 16, 16] \\
5 \(\times\) 5 Deconv\(\rightarrow\)ReLU\(\rightarrow\)BN & [32, 256, 8, 8] & 5 \(\times\) 5 Conv\(\rightarrow\)IReLU & [32, 256, 8, 8] \\
5 \(\times\) 5 Deconv\(\rightarrow\)ReLU\(\rightarrow\)BN & [32, 128, 16, 16] & 5 \(\times\) 5 Conv\(\rightarrow\)IReLU & [32, 512, 4, 4] \\
5 \(\times\) 5 Deconv & [32, 3, 32, 32] & Reshape & [32, 8192] \\ Tanh & [32, 3, 32, 32] & MLP & [32, 1] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The small CNN architecture for SoftGAN on CIFAR-10 [30]. The traning batch size is 32. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Generator** & **Output Shape** & **Discriminator** & **Output Shape** \\ \hline Noise & [32, 128] & Image & [32, 32, 32] \\ MLP\(\rightarrow\)ReLU\(\rightarrow\)Reshape & [32, 128, 4, 4] & 3 \(\times\) 3 ResBlock\(\times\) 2\(\rightarrow\)DownSample & [32, 128, 16, 16] \\
3 \(\times\) 3 ResBlock\(\times\)2\(\rightarrow\)UpSample & [32, 128, 8, 8] & 3 \(\times\) 3 ResBlock\(\times\)2\(\rightarrow\)Downsample & [32, 128, 8, 8] \\
3 \(\times\) 3 ResBlock\(\times\)2\(\rightarrow\)UpSample & [32, 128, 32, 32] & 3 \(\times\) 3 ResBlock\(\times\)2 & [32, 128, 8, 8] \\
3 \(\times\) 3 Conv & [32, 32, 32, 32] & Global Mean Pooling\(\rightarrow\)Reshape & [32, 128] \\ Tanh & [32, 32, 32, 32] & MLP\(\rightarrow\)sigmoid & [32, 1] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The ResNet architecture for SoftGAN on CIFAR-10 [30]. The traning batch size is 32. For quantitative comparison, we use the Inception Score [35] as the evaluation indicator, similar to CT-GAN [18], and the specific comparison results are shown in Table 4. As shown in the experimental results, the SoftGAN with the small CNN architecture is not bad, and the SoftGAN with ResNet surpasses all of the isomorphic unconditional GAN-based methods in the performance of the Inception Score. It is worth noting that some of the methods [36; 37] in Table 4 also use the multi-discriminator or multi-generator tricks. Theoretically, under the same architectural configuration, the trick of parallel subnetworks can improve the generation capability of the model. However, the SoftGAN still has a significant performance advantage in comparison. Furthermore, these techniques are fully orthogonal to our study, and they can be used together to enhance the model performance. Experiments with combinations of different network architectures and training techniques are presented in Section 6.5. In addition, we found that the dependence of our approach on the network architecture (small CNN: 6.46 \(\pm\) 0.42; ResNet: 8.55 \(\pm\) 0.05) is significantly lower compared with other methods, such as WGAN-GP [14] (small CNN: 2.98 \(\pm\) 0.11; ResNet: 7.86 \(\pm\) 0.07) and CT-GAN [18] (small CNN: 5.12 \(\pm\) 0.12; ResNet: 8.12 \(\pm\) 0.12). Whether in a simple network architecture or a complex network architecture, our proposed method has strong robustness and generates samples with considerable Inception Scores.