## 4 Evaluation
### Performance Comparison with Other Ensemble-Guided and Vanilla Models  
In order to evaluate our model, we compared the performance of the proposed model with those of the conventional mean-teacher method, the temporal ensembling models [24], and the vanilla model without any ensemble guidance. The consistency cost weight \(\beta\)  
Figure 4: Block diagrams of (**a**) the residual block and (**b**) CP-decomposed convolution block in Table 1. C in (**b**) is the number of channels of the residual block. Figure 5: Learning rate scheduling. and the tapering parameter \(\alpha\) of the conventional mean-teacher model were set to 2.5 and 0.99, respectively, which are the same as those of the proposed model. The time-varying weight \(w(n)\) was set as a ramp-up function as \(\exp[-5(1-r)]\) with linearly increased \(r\) from 0.0 to 1.0 during the first 80 epochs for the conventional temporal ensembling and mean-teacher models, which are the same as the values in [24; 25]. The consistency cost weight of the temporal ensembling was set to 2 because it showed the best performance among \([0.5,1,2,3,4,5]\). All the tested models had the same model size of 125.8 KB with 16-bit quantization after training, the same as in the vanilla model. Table 2 shows the representative performance of the proposed model with the performances of the vanilla model and other ensemble-guided models. Comparing the performance of the proposed model (log loss of 1.009 and F\({}_{1}\)-score of 67.12%) to that of the vanilla model (log loss of 1.052 and F\({}_{1}\)-score of 65. 79%), our ensemble-guided model can enhance the optimization and accuracy performances of the ASC model without increasing the model size. Simple application of temporal ensembling, which is a consistency learning technique for semi-supervised learning, can enhance slightly the log loss performance, but the F\({}_{1}\)-score performance is severely degraded. Simple application of the mean-teacher method can enhance slightly the F\({}_{1}\)-score, but the log loss performance is degraded. On the other hand, the proposed model can successfully enhance the performance, showing that our modification to suit to the supervised learning problem is meaningful. The temporal ensembling with modified \(w(n)\) as (5) and \(n_{act\_ens}\) of 260, which are the same as the settings of the proposed model, shows better performance than the conventional temporal ensembling method. However, the proposed model still performs better even though it does not require additional space to save averaged prediction results, whereas the temporal ensembling method requires the additional space. Table 3 shows the performance comparison with the state-of-the-art performances. The performance values of compared models in Table 3 are from the values reported in the original papers. The tested model with the proposed mean-teacher structure shows considerably improved performance compared to the baseline model, but slightly degraded performance compared to the state-of-the-art systems. They uses the same database to train their models, but have different training methodologies including extraction and pre-processing of features, learning rate control, optimizer, etc. Therefore, it is difficult to analyze exactly which factors cause the performance differences, but we think that the effective uses of the model lightweight and data augmentation methods may be the dominent factors. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Log Loss** &
\begin{tabular}{c} **Macro-Averaged** \\ **F\({}_{1}\)-Score** \\ \end{tabular} & **Model Size** \\ \hline Vanilla & 1.052 & 65.79\% & 125.8 KB \\ Conventional Temporal Ensembling & 1.042 & 62.26\% & 125.8 KB \\ Conventional Mean-Teacher & 1.069 & 66.47\% & 125.8 KB \\ Temporal Ensembling w/Unit Step \(w(n)\) & 1.023 & 65.93\% & 125.8 KB \\ Modified Mean-Teacher (Proposed) & **1.009** & **67.12\%** & 125.8 KB \\ \hline \hline \end{tabular}
\end{table}