## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{3}{*}{\begin{tabular}{c} **FM** \\ **Structure** \\ \end{tabular} } & \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{\begin{tabular}{c} **Compression** \\ **Ratio** \\ \end{tabular} } & \multicolumn{3}{c}{**SIM Partial Training (DR) *} & \multicolumn{3}{c}{**Merging Task Training**} & \multicolumn{1}{c}{\begin{tabular}{c} **Best** \\ **Feature** \\ **(\%)** \\ \end{tabular} } \\ \cline{3-8}  & & & & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Learning** & **Option** \\ \hline \multirow{8}{*}{\begin{tabular}{c} MFCMLFM \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} ACCHAR \\ \end{tabular} } & **1.20** & **90.77** & **0.91** & **98.80** & **0.99** & **0.001** & **DR(R)** \\  & & **2.00** & **89.56** & **0.90** & **98.80** & **0.99** & **0.001** & **DR(R)** \\ \cline{2-8}  & & & 3.00 & 85.95 & 0.86 & **96.39** & **0.96** & 0.01 & **DR(D)** \\ \cline{2-8}  & & & 1.20 & 85.31 & 0.85 & 94.92 & 0.94 & 0.01 & **DR(R)** \\ \cline{2-8}  & & & 2.00 & 87.57 & 0.88 & 94.07 & 0.95 & 0.01 & **DR(D)** \\ \cline{2-8}  & & & 3.00 & 83.05 & 0.83 & 94.07 & 0.95 & 0.001 & **DR(R)** \\ \hline \multirow{8}{*}{\begin{tabular}{c} MLFM \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} ACCHAR \\ \end{tabular} } & **1.20** & **90.36** & **0.90** & **98.80** & **0.99** & **0.005** & **DR(R)** \\  & & **2.00** & **89.16** & **0.89** & **98.80** & **0.99** & **0.0005** & **DR(R)** \\ \cline{2-8}  & & & 3.00 & 88.35 & 0.88 & **97.59** & **0.98** & 0.01 & **DR(R)** \\ \cline{2-8}  & & & **1.20** & **79.94** & **0.80** & **95.76** & **0.96** & **0.001** & **DR(D)** \\ \cline{2-8}  & & & & & & & & **Test(R)** \\ \cline{2-8}  & & & & & & & & **0.01** & **DR(R)** \\ \cline{2-8}  & & & **2.00** & **86.16** & **0.86** & **95.76** & **0.96** & **0.001** & **DR(R)** \\ \cline{2-8}  & & & 3.00 & 85.59 & 0.86 & 93.22 & 0.92 & 0.0005 & **DR(R)** \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} SLFM \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} ACCHAR \\ \end{tabular} } & **1.20** & **88.76** & **0.89** & **100.00** & **1.00** & **0.01** & **DR(R)** \\  & & 2.00 & 90.76 & 0.91 & **98.80** & **0.99** & 0.01 & **DR(R)** \\ \cline{1-1} \cline{2-8}  & & & 3.00 & 86.35 & 0.86 & **97.59** & **0.98** & 0.005 & **DR(R)** \\ \cline{1-1} \cline{2-8}  & & & **1.20** & **86.73** & **0.87** & **96.61** & **0.96** & **0.01** & **DR(R)** \\ \cline{1-1} \cline{2-8}  & & & 2.00 & 88.42 & 0.88 & **95.76** & **0.96** & 0.01 & **DR(R)** \\ \cline{1-1} \cline{2-8}  & & & 3.00 & 86.72 & 0.87 & **94.92** & **0.94** & 0.01 & **DR(R)** \\ \hline \end{tabular}

* * Accuracy (%) and F1-Score values of the SIM Partial Training are the averaged values of all Guest Parties’ which participating in the TT-VFDL-SIM (for classification).

\end{table} 
Table 14: Performance of the TT-VFDL-SIM with method option as DR (for classification) under various conditions. with the reconstruction performance of Table 7 with MSE of up to 0.00007 or less and \(R^{2}\) of at least 0.99. The SIM Partial Training mechanism introduces similar feature spaces to partial task training based on various method options and feature options. In the regression target task, feature option DD(R) or DD(D)-Test(R) of method option DD had the best performance. For method option DR of the classification target task, feature option DR(R) and DR(D)-Test(R) had the best performance. As a result, it can be said that extracting task-driven features based on the raw data leveraging a trained partial task model by introducing a similar feature space generated from decoded data is effective in improving the performance of TT-VFDL-SIM. The introduction of these similar feature spaces in SIM Partial Training can be seen as a kind of transfer learning mechanism. Meanwhile, the learning rate that can achieve the highest performance in merging task training basically differed depending on the type of FM Structure and dataset.