# Use of Generative Adversarial Networks (GAN) for
## 3 Results  
### Phase 1: Parameter Selection and Model Protocol  
There was a major improvement over previous modeling of the same dataset [1], with most models providing an accuracy close to 95% (Table 1). In this case, the differences among models were rather minor. Sequential models like VGG16 and Jason 2 performed very well and at least one of the parallel architectures (ResNet50) also performed very efficiently. As a matter of fact, the latter model provided the highest accuracy, the lowest loss  
Figure 3: Image resolution improvement by applying SRGAN twice.  
Figure 2: Sample of 10 generated images of tooth marks (**upper row**) and trampling (**lower row**).  
and the best-balanced accuracy, as reported by the F1-scores. Within these architectures, the choice of function had a minor impact in model performance (Table 1). The optimizer was more relevant to the final results. In most models, if considering the best two combinations for each model, SGD produced better performance of the network resulting in higher accuracy and lower loss (Table 1). SGD displayed the best performance of the \"relu\" (37.5%), the \"swish\" (12.5%) and the \"mish\" (12.5%) functions (adding results qualified as \"best\" and \"good\"). In contrast, Adam only managed to get a lower performance of the \"relu\" (25%) and the \"swish\" (12.5%) functions (Table 2). In total, 62.5% of the best performing combinations were achieved by SGD and only 37.5% was obtained with Adam. This, despite that three of the four best single combinations were obtained with Adam: Adam-relu (2), Adam-swish (1) and SGD-relu (1) (Table 2).  
The Bayesian networks supported this interpretation and expanded it. The \"BIC\" (score = \(-71.52577\)), the \"loglik\" (score = \(-56.68172\)), the \"bde\" (score = \(-72.85762\)), the \"bda\" (score = \(-72.85762\)), and the \"bda\" (score = \(-72.71031\)) hill-climbing score factors indicated by a majority of four to one, that the differences among the activation functions were not that relevant (Figure 4). It was the optimizer that made the difference.  
This analysis of parameter combinations suggests that, although several combinations must be tried for every dataset, the most parsimonious baseline model should be a combination of \"relu\" and SGD. Tuning is mandatory, since the best model obtained in this study  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Result = BEST** & & & \\
**optimizer** & & & \\  & **mish** & **relu** & **swish** \\
**Adam** & 0 & 0.25 & 0.125 \\
**SGD** & 0 & 0.125 & 0 \\ \hline
**Result = GOOD** & & & \\
**optimizer** & & & \\  & **mish** & **relu** & **swish** \\
**Adam** & 0 & 0 & 0 \\
**SGD** & 0.125 & 0.25 & 0.125 \\ \hline
**Result = NORMAL** & & & \\
**optimizer** & & **function** & \\  & **mish** & **relu** & **swish** \\
**[PERSON]** & 0.5 & 0.25 & 0.375 \\
**SGD** & 0.375 & 0.125 & 0.375 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proportion distribution of each activation function according to the two optimizers in the three categories: best (first best model), good (second best model) and normal.  
Figure 4: Bayesian network selection by the hill-climbing algorithm. Model (**A**), voted by majority of score functions; model (**B**), voted by the “loglik” function.  
was the combination of \"relu-SGD\" (accuracy = 97.9; F1-score = 0.8) and \"swish-SGD\" (accuracy = 98.4; F1-score = 0.77) under the ResNet50 architecture.  
Regarding the average performance of each architecture using all combinations, the ranking (based on accuracy) is as follows: ResNet50 (97.4%), VGG16 (95.8%), Jason2 (95.3%), and Densenet 201 (93.5%) (Figure 5).