## 3 Results
### Effects of the Level of Manipulation for the Recognizability of Input Image Features  
Effect on training loss: The level of manipulation for the recognizability of input image features showed the significant main effect on training loss, \(F(2,1.50)=5.94\), \(p=0.010\). However, pairwise comparison revealed no significant difference among \(\mathcal{I}_{\text{easy}}\), \(\mathcal{I}_{\text{fair}}\), and \(\mathcal{I}_{\text{clal}}\). The comparison of \(\mathcal{I}_{\text{fair}}\) vs. \(\mathcal{I}_{\text{clal}}\) only showed marginal tendency, \(-0.35(0.12),\ p=0.062\). Effect on test accuracy: The significant main effect of the level of manipulation for the recognizability of input image features on test accuracy was also found, \(F(2,0.86)=72.21\), \(p<0.001\). Pairwise comparison yielded a significant difference for both \(\mathcal{I}_{\text{easy}}\) vs. \(\mathcal{I}_{\text{clal}}\), \(0.04(0.02),\ p<0.001\), and \(\mathcal{I}_{\text{fair}}\) vs. \(\mathcal{I}_{\text{clal}}\), \(0.21(0.02),\ p<0.001\), which indicated that both \(\mathcal{I}_{\text{easy}}\) and \(\mathcal{I}_{\text{fair}}\) yielded better test accuracy over \(\mathcal{I}_{\text{clal}}\). No significant difference, however, was found between \(\mathcal{I}_{\text{easy}}\) and \(\mathcal{I}_{\text{fair}}\). \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Performance** & \multicolumn{4}{c}{**Factor 1**} & \multicolumn{4}{c}{**Factor 2**} & \multicolumn{4}{c}{**Interaction**} \\ \cline{2-10}
**Metrics for CNN** & **AAFT(1)** & **AAFT(2)** & **AAFT(3)** & **AAFT(4)** & **AAFT(5)** & \(\mathcal{I}_{\text{easy}}\) & \(\mathcal{I}_{\text{fair}}\) & \(\mathcal{I}_{\text{clal}}\) & \(\mathcal{I}_{\text{clal}}\) & \(\mathcal{p}\)**-Value** \\ \hline Training Loss & 3.25 (3),(4),(5) & 3.16 (3),(4),(5) & 2.56 (1),(2),(5) & 2.12 (1),(2),(5) & 1.37 (1),(2),(3),(4) & 2.49 & 2.32 & 2.66 & **0.025** \\ Test Accuracy (\%) & 73.3 (3),(4),(5) & 79.1 (5) & 88.9 (1),(5) & 91.9 (1) & 98.5 (1),(2),(3) & 95.7 (5) & 91.9 (5) & 71.4 (5) & **0.009** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Two metrics of convolutional neural network (CNN) performance. Value represents the mean for the five levels of data augmentation by AAFT (Factor 1) and the three levels of manipulation for the recognizability of input image features (Factor 2), and \(p\)-values for interaction effect. Superscripts represent significant differences from the other main effects conditions resulting from [PERSON] adjusted pairwise comparison. Interaction Effects of the Level of Data Augmentation by AAFT \(\times\) the Level of Manipulation for the Recognizability of Input Image Features  
Interaction effect on training loss: The analysis revealed that there was a significant interaction effect between the level of data augmentation by AAFT and the level of manipulation for the recognizability of input image features in training loss, \(F(8,0.40)=2.43\), \(p=0.022\), as shown in the rightmost column in Table 3. From Figure 5(a), we can see that the train losses of both \(\mathcal{I}_{\text{easy}}\) and \(\mathcal{I}_{\text{fair}}\) were greater than that of \(\mathcal{I}_{\text{chal}}\) under AAFT(1). They tended to be decreased quickly and became less than that of \(\mathcal{I}_{\text{chal}}\) as the level of data augmentation increased. This indicates that the level of manipulation for the recognizability of input image features might have had a different effect on training loss depending on \(\mathcal{I}_{\text{easy}}\), \(\mathcal{I}_{\text{fair}}\), and \(\mathcal{I}_{\text{chal}}\). Pairwise comparison revealed a marginal tendency for \(\mathcal{I}_{\text{fair}}\) vs. \(\mathcal{I}_{\text{chal}}\) under AAFT(5), \(-0.39(0.14),\ p=0.061\), but no significant difference was found.