# Few-Shot Wideband Tympanometry Classification in
## 4 Discussion  
In this article, we focused on the few-shot WBT classification in tosclerosis and proposed a novel domain adaption algorithm named GPGDA to deal with the inevitable domain shift in deep transfer learning methods. The proposed algorithm employed the Gaussian processes to explicitly calibrate the distributions of the feature extractor's outputs of the adopted backbone network between synthetic source dataset and realistic target dataset after pre-training, and the difference in distribution calibration was leveraged to optimize the parameters of the feature extractor with the help of gradient backpropagation algorithm. In addition, the classifier was trained on the realistic target dataset by using the conventional cross-entropy loss as an independent source. Furthermore, training was performed alternately on the source and the target datasets to the end of epochs in the proposed algorithm. The algorithm was studied empirically on an open-source WBT dataset with 80 tosclerosis samples and 55 control samples to verify its performance for the WBT-based tosclerosis diagnosis task. All of the listed performance results were obtained through a five-fold cross-validation and Monte Carlo test to ensure the reliability of evaluation as much as possible. Our algorithm yielded AUC values of \(97.6\pm 1.1\) percent, \(97.8\pm 1.2\) percent, and \(97.9\pm 1.1\) percent and F1-scores of \(95.7\pm 1.0\) percent, \(96.0\pm 1.1\) percent, and \(95.7\pm 0.9\) percent when the different synthetic source datasets _SynDatal_, _SynDatalN_, and _SynDataM_ were employed, respectively (Table 1). Its performance results were usually superior to those of the baseline approaches in FT and DAN. In a further analysis, we showed that there was an optimal value of approximately 0.5 for the hyperparameter \(\lambda\) in the proposed algorithm (Table 2). The algorithm could almost degenerate to FT if \(\lambda\) took a too small value and showed unstable testing performance if the value of \(\lambda\) was too large. Moreover, it was also verified that the loss \(l^{GP}\) calculated by Gaussian processes dominated the performance gains of the proposed algorithm compared with the cross-entropy loss \(l^{CE}_{r}\) via a set of experiments (Table 3). The FT approach, which did not exploit the domain shift information, produced lower AUCs and F1-scores than the existing DAN and the proposed GPGDA (Table 1). This indicated that there were non-negligible differences for the tosclerosis diagnosis between the distribution of the synthetic source dataset generated by data augmentation schemes and the distribution of the realistic target dataset. Therefore, explicitly considering the distribution calibration (domain adaptation) during the network training seems to be helpful in increasing the diagnostic accuracy of tosclerosis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Labeled** & **Unlabeled** & **AUC/_SynDatal_** & **AUC/_SynDatalN_** & **AUC/_SynDatalM_** \\ \hline
72 & 0 & \(93.13\pm 0.64\) & \(93.62\pm 0.82\) & \(93.85\pm 0.57\) \\
72 & 36 & \(94.03\pm 0.93\) & \(94.57\pm 0.50\) & \(94.91\pm 0.69\) \\
108 & 0 & \(97.61\pm 1.13\) & \(97.75\pm 1.21\) & \(97.88\pm 1.10\) \\ \hline \hline \end{tabular} Values are expressed as mean \(\pm\) standard deviation (\(r=10\) experimental replicates) of percentages. \(p<0.05\) for varying numbers of labeled and unlabeled data on AUC; two-way ANOVA, [PERSON]’s multiple comparison test, adjusted \(p\)-values for multiple comparisons.  
\end{table} 
Table 4: AUC performance of GPGDA algorithm with and without the additional unlabeled data. Our GPGDA provided a significant improvement in comparison with DAN (Table 1), and this could be explained by a more performant domain adaptation in our specific task of WBT-based diagnosis. It could be potentially attributed to the information employed to guide the distribution alignment.