# Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
## 4 Discussion  
Deep learning models generally have achieved greater success due to the availability of massive datasets and extended model depth and parameterization. However, in practice, factors such as memory and computation time during training and testing are important factors to consider when choosing a model from a large number of models. In addition, the success of deep learning also depends on the training data and the model generalization, which is very important for deploying models in practical use because it is difficult to collect training data and train individual models for all \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Modality** & **Preprocessing** & **Accuracy** \\ \hline SignFi [1] (9-layer CNN) & combination & no & **-** \\ \hline SignFi [1] (9-layer CNN) & combination & Phase Shift & 86.66\% \\ \hline HOS-Re [3] (SVM) & combination & no & 96.23\% \\ \hline Single-stream ResNet18 & combination & no & 93.60\% \\ \hline Single-stream ResNet18 & combination & SVD\_20 & 94.20\% \\ \hline Single-stream ResNet50 & combination & no & 95.24\% \\ \hline Single-stream ResNet50 & combination & SVD\_20 & 95.53\% \\ \hline Dual-output two-stream with ResNet18 & combination + difference & no & 91.18\% \\ \hline Dual-output two-stream with ResNet18 & combination + difference & SVD\_20 & 94.67\% \\ \hline Dual-output two-stream with ResNet50 & combination + difference & no & 95.75\% \\ \hline Dual-output two-stream with ResNet50 & combination + difference & SVD\_20 & 95.88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons of dual-output two-stream network with other methods on Lab150 group. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Modality** & **Preprocessing** & **Accuracy** \\ \hline SignFi [1] (9-layer CNN) & combination & no & 92.21\% \\ \hline SignFi [1] (9-layer CNN) & combination & Phase Shift & 94.81\% \\ \hline HOS-Re [3] (SVM) & combination & no & 96.34\% \\ \hline Single-stream ResNet18 & combination & no & 95.97\% \\ \hline Single-stream ResNet18 & combination & SVD\_20 & 95.95\% \\ \hline Single-stream ResNet50 & combination & no & 96.67\% \\ \hline Single-stream ResNet50 & combination & SVD\_20 & 96.60\% \\ \hline Dual-output two-stream with ResNet18 & combination + difference & no & 96.3\% \\ \hline Dual-output two-stream with ResNet18 & combination + difference & SVD\_20 & 96.75\% \\ \hline Dual-output two-stream with ResNet50 & combination + difference & no & 96.78\% \\ \hline Dual-output two-stream with ResNet50 & combination + difference & SVD\_20 & 97.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of dual-output two-stream network with other methods on Lab+Home276 group. different environments. In other words, the generalization capability is more important for practical use. According to the evaluation results shown in Table 5, our method has better practicability than other methods. The diversity of input data is very helpful in CNN-based methods. CNN-based methods extract features through training. Input diversity means that CNN can extract more types of features. This can avoid overfitting during network training. Therefore, the proposed dual-output two-stream network uses two modalities of input data and achieves good performance. Moreover, input data containing redundant and irrelevant noise must be preprocessed. This can be proved in the above experiments. Tables 3-6 show that SVD preprocessing can improve the performance of our dual-output two-stream network. In this study, the experimental results also show that deep learning is not always successful. It can be seen from Table 6 that the HOS-Re method obtains the best result. This method is a traditional machine learning method. It relies on manual feature engineering to calculate a large number of features and use SVM as a classifier. The method is different from CNN-based methods such as SignFi method and our method, which can automatically extract features through training.