# FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization
## 4 Experiment
### Evaluation in CWRU Bearing Dataset
Table 1 demonstrates that models from 2 to 6 highlight a combination of a common feature extractor and a classifier. Among the models, models 1, 2, 3 and 4 are directly generalized on the target domain after training on the source domain data. Conversely, models 5, 6 and 7 are approached using adversarial training. In the 6 th model, a fully connected classifier and a relational classifier, based on a relational module, are applied. Consequently, generalization testing produces two accuracies. The results from Models 4 and 5 clearly indicate that the generalization performance of domain adversarial models, which utilize both fully connected network classifiers and relational classifiers with relational modules, is comparable. However, the accuracy on the target domain declines following domain adversarial use of purely relational classifiers, as demonstrated by Models 4 and 6. Intriguingly, a generalization test conducted at each model parameter update reveals that the adversarial training of relational classifiers is associated with a gradual decrease in generalization accuracy. Therefore, it is imperative to augment relational classifiers with fully connected classifiers to achieve meaningful results. The adversarial model using fully connected (FC) and relational classifiers achieves accuracy rates of 82.6% and 81%, respectively. This improvement is observed in both FC and relational comparison classifiers. The feature extractor is responsible for this improvement in generalization performance as it extracts features with better generalization capabilities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Number**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Classifier in Training**} & \multicolumn{2}{c}{**Generalization Accuracy/\%**} \\ \cline{3-6}  & & **FC** & **Relation** & **None** & **FC** & **Relation** \\ \hline
1 & SVM & & & 73.0 & \\
2 & 1 DCNN + FC & ✓ & & 74.3 & \\
3 & 2 DCNN + FC & ✓ & & 79.1 & \\
4 & 2 DCNN + Relation & ✓ & & 78.8 \\
5 & 2 DCNN + FC + DA(DANN) & ✓ & & 80.1 & \\
6 & 2 DCNN + Relation + DA & & ✓ & 65 \\
7 & 2 DCNN + FC + Relation + DA + DA(MCDANN) & ✓ & ✓ & **82.6 * & **81 *** \\ \hline \hline \end{tabular}  
* The bold number represents top accuracy with mixed classifier.  
\end{table} 
Table 1: Experimental comparison of models. **Existing Model Contrast of FSN. ** After testing that the promotion can be brought from a mixed classifier, we conducted experiments on FSN with other classical models used for domain generalization. The empirical risk minimization-based method (ERM) is first used for comparison. Although many methods have been proposed for domain generalization, which are intuitively reasonable and technically feasible, most of them can only achieve a small performance improvement [32; 33; 34].Therefore, in this paper, the ERM method is used as one of the methods for comparison with the FSN network. On the basis of the above, in order to prove that FSN takes advantage of the character that domain labels have actual physical meaning, both single-source method and multi-source method experiments are carried out on FSN. Since the FSN network needs to train a network that can shift the high-domain features to the nearest low-domain features, there is no single-source method for the FSN network, which requires at least two domains with adjacent labels. Moreover, because the features need to be shifted into the feature space of adjacent domains during generalization, the FSN network must use domain 1 and domain 2 for the dual-source domain experiment. To facilitate comparison, all models are trained on domain 1 and domain 2 with dual source domains, and then tested on domain 3 for generalization. In the multi-source domain experiment, all source domains 0 to 2 are used during training, and domain generalization is tested on target domain 3. In addition, all methods are trained with the relational classifier introduced in Section 4 to improve generalization.