# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 3 Experiments and Results Analysis
### Results Analysis  
#### 3.3.1 Results Analysis of the Vision Data Experiment  
Utilizing the cross-domain mixed datasets introduced earlier, six cross-domain transfer learning tasks can be established. To make the experimental results statistically more significant, each transfer learning task is repeated ten times, with mean, standard deviation, and maximum values selected as indicators to compare the performance of different methods. The experimental results, based on the output features of the sixth hidden layer (fc6) are shown in Tables 1 and 2. It can be observed that, among all comparative methods, the category dictionary augmented transfer learning model demonstrates the best overall performance. In four out of six transfer tasks, the model achieves the highest classification accuracy in terms of mean value, while also attaining the best results for maximum values in three classification tasks. For a better visualization of the model's performance, bar charts of data from Tables 1 and 2 are depicted in Figures 6 and 7, respectively. Figure 5: SSDT model-based bearing fault diagnosis process. Comparing the experiments with a 1-NN model, it is apparent that, despite containing a limited number of labeled samples in the target domain, relying solely on existing labels within the target domain does not enable the classifier to achieve good generalization ability overall. Although the 1-NN model may yield satisfactory classification results in some scenarios, its output exhibits significant fluctuations due to variances. Introducing source domain samples to assist in completing target domain classification tasks has a positive effect on enhancing classifier generalization performance. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Task** & **1-NN [25]** & **SRC [26]** & **LSDT [27]** & **TSC + LR [4]** & **SSDT** \\ \hline L\(\rightarrow\)V & 49.12 \(\pm\) 5.58 & 51.71 \(\pm\) 4.48 & 49.80 \(\pm\) 4.49 & 53.96 \(\pm\) 4.51 & **55.37 \(\pm\) 6.59** \\  
In the experiments, the transfer learning models (LSDT, TSC) exhibit improved classification performance compared to non-transfer learning models (1-NN, SRC). This indicates that incorporating cross-domain transfer operations contributes to the integration of source and target domain data, promoting efficient utilization. In summary, compared to baseline methods, the category dictionary augmented transfer learning model demonstrates stronger competitiveness in performance. This is partly attributed to inheriting the SRC, and the strategies in the literature that propose using category dictionaries for residual reconstruction to complete label predictions without introducing additional classifiers. Furthermore, the model establishes strong correlation constraints between source and target domains based on limited labeled samples in the target domain, using conditional probability distribution to facilitate knowledge transfer across different domains. The experimental results based on the output features of the seventh hidden layer (fc7) are presented in Tables 3 and 4. Similar to the outcomes with fc6 features, the category dictionary augmented transfer learning model exhibits the most exceptional overall performance among all experimental methods. The model's mean value achieves the best results in the L\(\rightarrow\)V, L\(\rightarrow\)S, V\(\rightarrow\)L, and V\(\rightarrow\)S transfer tasks, while the maximum values obtain the best outcomes in the L\(\rightarrow\)V, V\(\rightarrow\)L, and V\(\rightarrow\)S transfer tasks. Furthermore, LSDT and TSC models perform best in the S\(\rightarrow\)L and S\(\rightarrow\)V cross-domain tasks, respectively. In the remaining cross-domain tasks, they outperform SRC and 1-NN models. These findings reiterate that the introduction of auxiliary domain data can indeed improve model performance compared to non-transfer classifiers.