## 4 Evaluation
### Performance Comparison with Various Parameters  
Table 4 shows the performances of the proposed model with various consistency weights. For \(\beta=0\), the performance is of course similar to that of the vanilla model. In our experiment, the performance is the best when \(\beta=2.5\). The performances of the proposed model in the cases of \(\beta=5\) and \(\beta=7.5\) are slightly lower than the best performance, but these cases have better performance than the case with \(\beta=0\), which is practically the same as that of the vanilla model. As mentioned in Section 3.3, our proposed model has modified strategies for consistency learning: removing unlabeled data path, modification of the time-varying weight function \(w(n)\), and modification of adding noise strategy. In this section, we present the results of additional experiments to validate and to the second and third modification. The first modification, removing unlabeled data path, is not verified because it is obvious. The effectiveness of the modification of \(w(n)\) is shown in Table 5, and the effect of adding noise strategy is evaluated in Tables 6 and 7. Table 5 shows the performances of the proposed model with various start epoch parameters \(n_{act\_ems}\). As mentioned earlier, the training of the vanilla model was saturated between 230 and 260 epochs, and the best performance was obtained when ensemble-guided learning was started at 260 epochs, which is immediately after saturation. Early application of the ensemble-guided learning, such as \(0\leq n_{act\_ems}\leq 100\), seems to interfere in the early-stage of supervised learning due to the ensembles of immature student models. Moreover, the last row in Table 5 shows the performance of the proposed system when only \(w(n)\) is replaced with an exponential ramp-up function for 260 epochs. Comparing the result of the exponential ramp-up \(w(n)\) with the results of the unit step \(150\leq n_{act\_ems}\leq 300\), the results of unit step weights are better than that of the exponential ramp-up weight function. Table 6 shows the performances of the proposed model with various signal-to-noise ratios (SNRs) when adding Gaussian noise. As mentioned earlier, the noise was added to the student model inputs only in the activated period of the ensemble-guided model (i.e., \begin{table}
\begin{tabular}{c c c} \hline \hline
**Consistency Weight** & **Log Loss** & **Macro-Averaged F1-Score [\%]** \\ \hline \(\beta=0\) & 1.045 & 65.66\% \\ \(\beta=2.5\) & **1.009** & **67.12\%** \\ \(\beta=5\) & 1.013 & 66.07\% \\ \(\beta=7.5\) & 1.019 & 66.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with various consistency weights, \(\beta\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Start Epoch** & **Log Loss** & **Macro-Averaged F1-Score** \\ \hline \(n_{act\_ems}=0\) & 1.060 & 63.55\% \\ \(n_{act\_ems}=50\) & 1.048 & 64.32\% \\ \(n_{act\_ems}=100\) & 1.049 & 65.53\% \\ \(n_{act\_ems}=150\) & 1.040 & 66.04\% \\ \(n_{act\_ems}=200\) & 1.022 & 66.20\% \\ \(n_{act\_ems}=260\) & **1.009** & **67.12\%** \\ \(n_{act\_ems}=300\) & 1.027 & 65.70\% \\ \hline \hline Ramp-up for 260 epochs & 1.045 & 65.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison with various start epoch parameters \(n_{act\_ems}\) for ensemble guidance. when \(n\geq n_{act\_ems}\)). In the 70-dB SNR case, the noise is too small, so the performance is very similar to that of the model trained without noise. The case with 60-dB SNR has the best performance among the various SNR settings. For the 50-dB SNR case, the performance is slightly worse than the best performance, probably due to the excessive noise, but it performs better than the no-noise case. Table 7 shows the performance comparison between noise addition strategies of the conventional and proposed methods. The conventional mean-teacher model does not recommend any specific noise addition strategy but generally uses a strategy of adding noise to each model's input throughout the training process for semi-supervised learning.