# Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images
## 3 Experiment Results  
For verification of the inference accuracy of personal visual preferences from the proposed model, we utilized the three public benchmark datasets Fashion-MNIST, LFW, and Indoor Scene Recognition [16; 17; 18]. Before training the proposed model, all images from these datasets were classified and labeled as preferred or non-preferred, based on the preferences of a specific user. Table 1 shows the ratios of the two classes (preferred and non-preferred) for the Fashion-MINIST, LFW, and Indoor Scene Recognition databases, according to the user's preferences. In the process of determining the subject's preference for each image, a subject was asked to determine a preference for each image within 1 s in order to maximally exclude interference from other factors including the subject's intrinsic episodic memory or other non-visual factors. In addition, in order to minimize the effect of the subject's fatigue,  
Figure 5: Labeled Faces in the Wild (LFW) dataset images and labels [6; 17]. Figure 6: Indoor Scene Recognition dataset images and labels [18]. the entire image database was divided into several subgroups. Additionally, a subject was asked to decide preference instantly and sensibly based on visual characteristics as much as possible. We considered two different models. Model 1 was trained and tested using only a training dataset and a test dataset. Model 2 utilized a validation dataset as well as training and test datasets. In the experiments for measuring classification performance of the proposed preference inference model using the Fashion-MNIST database, Model 1 used 60,000 training images and 10,000 test images. However, for Model 2, training was conducted with 50,000 training images, 10,000 validation images, and 10,000 test images, as shown in Figure 7. Table 2 shows the preference classification results for Model 1 and Model 2 with the Fashion-MNIST dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fashion-MNIST** & **LFW** & **Indoor** \\  & & & **Scene Recognition** \\ \hline \multirow{2}{*}{**Preferred**} & 57.92\% & 55.93\% & 54.77\% \\  & (40,545/70,000) & (5034/9000) & (8246/15,000) \\ \multirow{2}{*}{**Non-preferred**} & 42.08\% & 44.07\% & 45.03\% \\  & (29,455/70,000) & (3966/1000) & (6754/15,000) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data classification ratios of user preferences in each dataset. Figure 7: Schematics for datasets used with Model 1 and Model 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Data Set:**} & \multicolumn{3}{c}{**Preference Classification Results**} \\
**Fashion-MNIST** & \multicolumn{3}{c}{**Model 1**} & \multicolumn{3}{c}{**Model 2**} \\ \hline \multirow{6}{*}{Training dataset} & TP & FP & TP & FP \\  & (31,889/60,000) & (2876/60,000) & (25,512/50,000) & (20,859/50,000) \\ \cline{2-5}  & FN & TN & FN & TN \\  & (602/60,000) & (24,633/60,000) & (391/50,000) & (3238/50,000) \\ \cline{2-5}  & \multicolumn{3}{c}{**Correct Accuracy**} & \multicolumn{3}{c}{**Correct Accuracy**} \\  & **(TP + TN)/(TP + FP + FN + TN)** & **(TP + TN)/(TP + FP + FN + TN)** \\  & **94.20\%** & **92.74\%** \\  & **(56,522/60,000)** & **(46,371/50,000)** \\ \hline \multirow{6}{*}{Test dataset} & TP & FP & TP & FP \\  & (5229/10,000) & (137/10,000) & (5347/10,000) & (60/10,000) \\ \cline{2-5}  & FN & TN & FN & TN \\  & (651/10,000) & (4083/10,000) & (433/10,000) & (4160/10,000) \\ \cline{2-5}  & \multicolumn{3}{c}{**Correct Accuracy**} & \multicolumn{3}{c}{**Correct Accuracy**} \\  & **(TP + TN)/(TP + FP + FN + TN)** & **(TP + TN)/(TP + FP + FN + TN)** \\  & **93.12\%** & **95.07\%** \\  & **(9312/10,000)** & **(9507/10,000)** \\ \hline \multirow{6}{*}{Validation data-set} & TP & FP \\  & & (5280/10,000) & (53/10,000) \\ \cline{2-5}  & FN & TN \\  & & (755/10,000) & (3932/10,000) \\ \cline{2-5}  & & **Correct Accuracy** \\  & & **(TP + TN)/(TP + FP + FN + TN)** \\  & **92.12\%** & **9212/10,000)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Preference classification results with the Fashion-MNIST dataset.