# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 4 Experiments
### Experimental Results Analysis
#### 4.3.3 Visualization
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Method**} & **JSRT** & **Montgomery** & **Office–Home** \\  & **BAC (\%)/F1 Score (\%)** & **BAC (\%)/F1 Score (\%)** & **BAC (\%)/F1 Score (\%)** \\ \hline Transformer & 71.05 \(\pm\) 2.94/69.23 \(\pm\) 2.78 & 71.23 \(\pm\) 2.88/69.50 \(\pm\) 3.12 & 70.89 \(\pm\) 2.56/68.89 \(\pm\) 2.65 \\ DANN & 80.01 \(\pm\) 2.75/78.50 \(\pm\) 3.01 & 80.50 \(\pm\) 3.05/79.03 \(\pm\) 2.96 & 79.84 \(\pm\) 2.69/78.21 \(\pm\) 2.72 \\ LPJT & 79.96 \(\pm\) 3.10/77.08 \(\pm\) 2.89 & 80.14 \(\pm\) 3.20/77.64 \(\pm\) 2.75 & 80.48 \(\pm\) 2.93/77.46 \(\pm\) 3.07 \\ CDAN & 80.23 \(\pm\) 2.57/79.04 \(\pm\) 2.64 & 80.32 \(\pm\) 2.48/79.27 \(\pm\) 2.59 & 80.05 \(\pm\) 2.71/78.45 \(\pm\) 2.99 \\ MMTADAN & 96.00 \(\pm\) 2.55/95.01 \(\pm\) 2.82 & 96.50 \(\pm\) 2.62/95.25 \(\pm\) 2.98 & 96.25 \(\pm\) 2.53/94.93 \(\pm\) 2.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average classification performances for different datasets. Figure 7: ROC curves of different models for the Montgomery dataset. result for the Office-Home dataset (76.5% BAC) is lower than those for the JSRT and Montgomery datasets, highlighting its limitations in specific contexts. The CDAN also exhibited a decrease in its performance on the Office-Home dataset (77% BAC), possibly due to the complexity of the data, affecting the model's generalization capability. Despite the MMTADAN's excellent performance on the JSRT and Montgomery datasets (96% and 96.5% BACs, respectively), its BAC for the Office-Home dataset is 90%. This result indicates that the MMTADAN maintained a relatively stable performance across the different datasets, although it was somewhat affected on the Office-Home method, and it remains the best-performing method among all of them. Furthermore, Table 6 displays the \(p\)-value calculations and 95% confidence intervals for the different methods for the Office-Home dataset. Statistical analysis reveals that the MMTADAN showed a significant advantage in the classification performance, with its BAC and F1 scores markedly higher than those of the other methods. Through rigorous statistical analysis, we confirmed the reliability of the experimental results and provide valuable references for subsequent research. Using Big O notation, we estimated the time complexity of each method. Subsequently, after training each method, we recorded the time required for the model inference in detail. These data aided us in comparing the performances of the different models to evaluate their feasibility and advantages in practical applications. The time complexities were as follows: Transformer: O(\(n\)-\(d^{2}\)) (where n is the sequence length and d is the feature dimension); DANN: O(\(n\)-\(d\)-\(m\)) (where \(n\) is the number of samples and m is the number of features); LPJT, CDAN, and MMTADAN: the complexity was analyzed based on specific implementations. The results are shown in Table 7.  
As shown in Table 7, the MMTADAN had the shortest training time at only 90 s, indicating high efficiency during the model training. In contrast, the Transformer took the longest time to train, at 150 s, likely due to its complex architecture and computational overhead from the self-attention mechanism. The MMTADAN also excelled in the inference time, requiring only 10 s, making it more advantageous for real-time applications. Other methods have inference times ranging from 15 to 20 s, indicating lower efficiencies during inference. The MMTADAN outperformed other methods in both the training and inference times, showcasing its exceptional computational efficiency and performance. Therefore, the MMTADAN is regarded as the best-performing method in this experiment.