# Personalized Advertising Design Based on Automatic Analysis of an Individual's Appearance
## 5 Experiments
### Transfer Learning Architectures  
In an effort to evaluate the performance of the different architectures in a general manner, an experiment was conducted where each available architecture was trained for a limited number of epochs, and the average accuracy was measured after this initial training phase. The results of this experiment are depicted in Figure 15. The x-axis represents the scale of network size in terms of the number of parameters, while the y-axis represents the average accuracy achieved after training for 10 epochs. To optimize both the size of the  
Figure 14: Class Embedding2048 Learning curves: (**a**) Training loss, (**b**) Testing loss, (**c**) Training accuracy, (**d**) Testing accuracy  
Figure 13: LIP autoencoder prediction examples. architecture and the average accuracy, we aimed to determine the highest accuracy that could be achieved with the lowest number of parameters. Therefore, the global maximum would be located at the coordinate \((0,1)\). Based on this experimentation, four architectures were chosen to undergo complete training: MobileNet v2 [63], Densenet121 [64], VGG16 [48], and XCEPTION [65]. These architectures were trained under the conditions specified in Table 5. The training curves for the loss and mean accuracy of the four models are depicted in Figure 16. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Inputs**} \\ \hline Dataset: & Style7 & Training samples: & 9161 \\ Input dimensions: & \(128\times 128\times 3\) (RGB) & Testing samples: & 1000 \\ Normalized dataset: & No & Total samples: & 10,161 \\ \hline \multicolumn{3}{c}{**Hyperparameters**} \\ \hline Optimizer: & Adam & Learning rate: & 0.01 \\ Loss function: & Categorical & Normalized weights: & No \\ Training epochs: & 400 & Softmax at output: & No \\ Regularization: & No & Output format: & One-hot \\ Output size: & 7 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transfer learning model hyperparameters. Figure 15: Transfer learning architectures: pilot test. Figure 16: Testing curves: (**a**) MobilenetV2 testing loss, (**b**) MobilenetV2 testing accuracy, (**c**) MobilenetV2 testing loss, (**d**) MobilenetV2 testing accuracy, (**e**) DenseNet testing loss, (**f**) DenseNet testing accuracy, (**g**) XCEPTION testing loss, (**h**) XCEPTION testing accuracy. The accuracy results for the transfer learning training are presented in Table 6. Among the tested architectures, the model based on XCEPTION achieved the best performance in terms of error and accuracy while maintaining a reasonable training time. To validate the effectiveness of the automatic generation of personalized ads, we conducted an experiment involving 100 participants. This sample size was determined to be statistically valid according to the central limit theorem, which requires that data be sampled randomly, samples be independent of each other, and the sample size be sufficiently large but not exceed 10% of the population. Our study covered two schools with approximately 1200 students, making the selected sample size of 100 participants adequate. Please refer to Table 7.  
The table provides a concise overview of the significant aspects of the study's results. It highlights the sample size, which consisted of 100 participants, meeting the requirements for statistical validity based on the central limit theorem. Additionally, key demographic insights obtained from the socio-demographic survey are presented, indicating important characteristics of the participants. The training of transfer learning models was conducted to support the automatic generation of personalized ads. Table 6 presents a summary of the training results for various architectures. Notably, the XCEPTION model achieved outstanding performance, with the lowest error rate of 0.2267 and the highest accuracy of 0.9827. This suggests that the XCEPTION-based approach holds promising potential for accurately predicting styles, thus enabling effective personalized advertising generation.