# Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images
## 3 Experiment Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**LFW Dataset Classification Results**} & \multicolumn{3}{c}{**Indoor Scene Recognition Dataset**} \\ \hline \multirow{6}{*}{Model 1} & TP & FP & TP & FP \\  & (3657/9000) & (780/9000) & (6935/15,000) & (1105/15,000) \\ \cline{2-5}  & FN & TN & FN & TN \\  & (1377/9000) & (3186/9000) & (1311/15,000) & (5649/15,000) \\ \cline{2-5}  & **Correct Accuracy** & **Correct Accuracy** \\  & **(TP + TN)/(TP + FP + FN + TN)** & **(TP + TN)/(TP + FP + FN + TN)** \\  & **76.03\%** & **83.89\%** \\  & **(6843/9000)** & **(12,584/15,000)** \\ \hline \multirow{6}{*}{Model 2} & TP & FP & TP & FP \\  & (3643/9000) & (699/9000) & (6608/15,000) & (1332/15,000) \\ \cline{2-5}  & FN & TN & FN & TN \\  & (1397/9000) & (3267/9000) & (1638/15,000) & (5422/15,000) \\ \cline{2-5}  & **Correct Accuracy** & **Correct Accuracy** \\  & **(TP + TN)/(TP + FP + FN + TN)** & **(TP + TN)/(TP + FP + FN + TN)** \\  & **76.78\%** & **80.20\%** \\  & **(6910/9000)** & **(12,030/15,000)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Preference classification results from Model 1 and Model 2 with the Labeled Faces in the Wild (LFW) and Indoor Scene Recognition datasets. From the experimental results, we were able to conclude that both Model 1 and Model 2 performed well in classifying preferences with the Fashion-MNIST dataset. The classification performance of Model 2 was 95.07%, which was superior to Model 1 for the test dataset. For the purpose of evaluating the performance of the proposed personal preference model, we applied the proposed model (pre-trained with the Fashion-MNIST database) for preference classification with the LFW database and the Indoor Scene Recognition database. In this experiment, Model 1 and Model 2 were applied to classify the LFW data and Indoor Scene Recognition data into only two classes (preferred and non-preferred) without any additional training process for fine-tuning. The preference classification experiment using the LFW dataset was conducted by randomly selecting 9000 images from 13,233 images in LFW. In addition, in the experiments using the Indoor Scene Recognition dataset [18], 15,000 images were randomly selected from 15,620 images. Table 3 shows the experimental results of preference classification with the LFW dataset and the Indoor Scene Recognition dataset by Model 1 and Model 2. According to the experimental results in Table 3, the proposed model showed plausible preference classification performance for two databases with different characteristics from the prior-learning database. Additionally, in order to verify the effectiveness of transfer learning between inter-domain datasets with different visual characteristics, transfer learning was applied to the learning process of the proposed preference inference model. Table 4 shows the experimental results of preference classification with the LFW and Indoor Scene Recognition datasets by the proposed model considering transfer learning. As shown in Table 4, the proposed preference classification model reflecting transfer learning showed better performance than the model that did not reflect transfer learning, as shown in Table 3.  
From these experimental results, we can conclude that the proposed model was able to properly classify preferences for datasets with different visual characteristics through transfer learning.