## 3 Results and Discussion
### Subjective Evaluation  
Considering that the present work generates results based on visual perception and as many assumptions and possible conclusions have been obtained with the objective evaluation, we need an in-depth evaluation based on the experience of an expert in the field. Although all the previous results confirm that the performance of the activation functions is ReLU, Mish, Leaky ReLU, and Swish, we want a confirmation and an in-depth study based on a subjective evaluation. This subjective evaluation was approached by involving a professional who evaluated six important features of the images by adding a textual description for each of them. As can be seen in Figure 7, there are significant differences between the used activation functions. Figure 6: Performance of the method using ReLU as activation function. Figure 7: Performance of the different activation functions for the same example. In this case, we have randomly selected 24 images for each activation function, which have been qualitatively evaluated by an architect considering the following relevant aspects that can be seen in the image: background, general shape, shadows, building interior, level of detail, and level of sharpness. Table 6 compiles this information for each characteristic. If we evaluate the activation functions in depth, we can see the following common points. ReLU has a good definition in the interiors but has some problems discriminating between the temple and the background. The general shape is better defined with less blurring. It seems that it tries to represent the triglyphs with more coherence. It is the one with better sharpness. The shadows are coherent, and the background is well defined. It shows transparencies (a mix between the background and the part of the temple to be reconstructed) but to a lesser degree than the rest of the activation functions. As it has fewer cases of transparencies, it seems to make better reconstructions influencing the metrics as the similarities with the pixels of the ground truth are higher. It seems to make more accurate reconstructions instead of doing changes related to the overfitting (for example, it tries to reconstruct the corners while the other tends to round them). In the case of Leaky ReLU, the interiors make sense, but details cannot be appreciated (they lack fine details). It has low sharpness, creating marked lines, but it is not well defined and has no precise forms. The shadows are suitable. In general, the reconstruction is not of good quality. It has a problem with the straight lines. With less sharpness, it makes worse reconstructions, so it is very sensitive to the light saturation of the image. It creates many transparencies that could be understood as artifacts. This problem with the transparencies affects the metrics having a higher error. It seems that the transparencies are proportional to the indecision of the model. More transparency indicates more indecision. Regarding Swish performances, we can see that with the general shape, it performs similarly to ReLU. The shadows are coherent but seem a bit diffuse. The background is well reconstructed. It shows transparencies with less opacity compared to ReLU. It seems that the level of detail is better in particular architectural elements. It performs very defined reconstructions, but it has a lower sharpness due to the transparencies. In the interiors, we can see some artifacts. This problem with the artifacts affects the objective metrics, adding errors that prevent the evaluation of the reconstruction well and its coherence. The  
\begin{table}
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shadows seem to be darker concerning the ground truth images. The brightness points are derived from the reconstructions and the transparencies caused by them. The corners are very badly reconstructed due to the lack of contrast between both walls generated by diffuse light. This activation function blurs more the reconstruction, while Leaky ReLU adds more noise.