## 4 Experiment and Results
### Result on MICC-SRI Dataset  
We compared other methods based on unsupervised learning where the ground-truth labels were not used during training. We also added supervised training results using ground-truth labels during the training stage. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Discriminator Network**} \\ \hline
**Layer** & **Kernel Size** & **Stride** & **Output Size** \\ \hline Input & & & C \(\times\) 256 \(\times\) 256 \\ Encoder-conv 1 & 5 & 2 \(\times\) 2 & 64 \(\times\) 128 \(\times\) 128 \\ Encoder-conv 2 & 5 & 2 \(\times\) 2 & 128 \(\times\) 64 \(\times\) 64 \\ Encoder-conv 3 & 5 & 2 \(\times\) 2 & 256 \(\times\) 32 \(\times\) 32 \\ Encoder-conv 4 & 5 & 2 \(\times\) 2 & 512 \(\times\) 16 \(\times\) 16 \\ Encoder-conv 5 & 5 & 2 \(\times\) 2 & 512 \(\times\) 8 \(\times\) 8 \\ Encoder-conv 6 & 5 & 2 \(\times\) 2 & 512 \(\times\) 4 \(\times\) 4 \\ Fully Connected & & & 1024 \\ Output & & & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Discriminator architecture details. We reported our evaluation results in terms of the per-pixel score, F1, and mean intersection of union (mIOU). The per-pixel accuracy reveals the overall accuracy of the segmentation. The F1 and mIOU are used to calculate the average of the intersecting area of every class, showing the details of each class reconstruction performance. Moreover, the inference speed is as reported in terms of the frame per second, for which a higher value indicates a faster inference speed. As indicated in Table 3, the self-entropy term in AdvEnt [27] and our methods performed better than the vanilla GAN loss-term-based methods. This is because the self-entropy term directly stabilized the adversarial learning for the probability map. In addition, our preprocessing increases the performance, emphasizing the need to use a preprocessing method. The total variation proved that smoothing the label during training increases the performance. The details of the mIOU results per class are depicted in Table 4. The result reveals that the large area classes (i.e., sky, roads, and sidewalks) provide comparable results. This exhibits that adversarial training recovers the overall structural information but fails to give details due to the unstable training. On the other hand, the self-entropy methods (AdvEnt [27] and ours) showed best at restoring the detail classes (i.e., poles and fences). The qualitative results are illustrated in Figures 5 and 6. The first and third pictures demonstrate that the self-entropy methods try to recover more detailed information than the DCGAN, in which the sidewalk was recovered with the structural information. This can also be observed in the second example on the fence on the left side. The fence patterns were recovered plausibly. Moreover, our method offers more stable results that include less noise than the AdvEnt. The examples are observed in the fourth and fifth images of Figure 6, where the dynamic labels are filled with nearby static labels. In addition, the fifth example exhibits the smoothing effect on the sky and buildings, recovering the car, which demonstrates the effect of the entropy-based total variation loss function. More details of the result are illustrated in Figure 7, where the grayscale of the mask error map indicates the prediction error from the ground-truth labels, and the white space reveals the correct answer. The mask result represents the restored map in the dynamic map. For the first and