# Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images
## 3 Experiment Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**LFW Classification Results through Transfer**} & \multicolumn{2}{c}{**Indoor Scene Recognition Classification**} \\  & \multicolumn{2}{c}{**Learning**} & \multicolumn{2}{c}{**Results through Transfer Learning**} \\ \cline{2-8} \multicolumn{2}{c}{} & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} \\ \hline \multirow{4}{*}{Training data-set} & TP & FP & TP & FP & TP & FP & TP & FP \\  & (3823/8000) & (526/ & (3811/ & (522/ & (5988/ & (575/ & (5912/ & (447/ \\  & 8000) & 8000) & 8000) & 12,000) & 12,000) & 12,000) \\ \cline{2-8}  & FN & TN & FN & TN & FN & FN & TN \\  & (652/ & (2999/ & (664/ & (3003/ & (499/ & (4938/ & (375/ & (5066/ \\  & 8000) & 8000) & 8000) & 12,000) & 12,000) & 12,000) \\ \cline{2-8}  & \multicolumn{2}{c}{**Correct Accuracy**} & \multicolumn{2}{c}{**Correct Accuracy**} & \multicolumn{2}{c}{**Correct Accuracy**} & \multicolumn{2}{c}{**Correct Accuracy**} \\  & **(TP + TN)/(TP + FP +** & **(TP + TN)/(TP + FP +** & **(TP + TN)/(TP + FP +** & **(TP + TN)/(TP + FN + TN)** & **FN + TN)** & **FN + TN)** \\  & **FN + TN** & **85.18\%** & **91.05\%** & **91.48\%** \\  & **(6822/8000)** & **(6814/8000)** & **(10,926/12,000)** & **(10,978/12,000)** \\ \hline \multirow{4}{*}{Test data-set} & TP & FP & TP & FP & TP & FP & TP & FP \\  & (465/1000) & (70/1000) & (451/1000) & (64/1000) & (1599/3000) & (1585/3000) & (138/3000) \\ \cline{2-8}  & FN & TN & FN & TN & FN & TN & TN \\  & (94/ & (371/ & (108/ & (377/ & (160/3000) & (1022/ & (174/ & (1103/ \\  & 10000) & 10000) & 1000 & 1000 & 1000 & 30000 & 30000 & 30000 & 3000 \\ \cline{2-8}  & \multicolumn{2}{c}{**Correct Accuracy**} & \multicolumn{2}{c}{**Correct Accuracy**} & \multicolumn{2}{c}{**Correct Accuracy**} & \multicolumn{2}{c}{**Correct Accuracy**} \\  & **(TP + TN)/(TP + FP +** & **(TP + TN)/(TP + FP +** & **(TP + TN)/(TP + FP +** & **(TP + TN)/(TP + FP +** & **FN + TN)** & **FN + TN)** \\  & **FN + TN** & **FN + TN** & **FN + TN** & **FN + TN** & **FN + TN)** \\  & **83.60\%** & **82.80\%** & **87.36\%** & **89.60\%** \\  & **(836/1000)** & **(828/1000)** & **(2621/3000)** & **(2688/3000)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Preference classification results from Model 1 and Model 2 using transfer learning with the LFW and Indoor Scene Recognition databases. This transfer learning process might be recognized as playing an important role in constructing the personality of each person, reflecting visual preferences by incrementally determining the individually preferred characteristics for visual information. Thus, we were able to conclude that, through numerous experiences with visual information that is tremendously diverse in characteristics, each human shapes their own preferences for visual data. Finally, we applied a Grad-CAM model for the purpose of explaining the reasons for the proposed model's preference classifications [19]. Grad-CAM generated an attention heat map to provide explanations for the classification results of the specific user's preferences [19]. Figure 8 shows the visual attention map for the results of preferred and non-preferred classifications from the LFW dataset. Figure 8 shows some heat map results obtained by Grad-CAM for the correctly classified preferred images in the LFW dataset. Figure 8 shows some of the heat map results obtained by Grad-CAM for the correctly classified non-preferred images in the LFW dataset. According to the subject's opinions, non-facial visual features including a suit or tie had some influence on the preference of the face image when deciding the preference for each face image of LFW. In addition, it was judged that the racial issue or the presence of acquaintances was also partly affected, even though a subject was asked to decide preference momentarily and sensibly based on the visual face as much as possible.