# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 4 Experiments
### Experimental Results Analysis
#### 4.3.2 Ablation Experiments  
To further elucidate the impacts of the pseudo-label classifier and feature fusion modules within the MMTADAN on the image classification performance, we compared the algorithm without the pseudo-label classifier (NLC) and the algorithm without the feature fusion module (NFF). Using the Office-Home dataset, four transfer tasks were set up, with the results are displayed in Table 3. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Task** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Ar \(\rightarrow\) Pr & Transformer & 60.34\% & 65.48\% & 50.12\% & 0.57 \\  & DANN & 70.43\% & 75.32\% & 55.78\% & 0.64 \\  & LPJT & 68.29\% & 70.41\% & 52.16\% & 0.60 \\  & CDAN & 75.84\% & 78.23\% & 65.74\% & 0.71 \\  & MMTADAN & 85.67\% & 88.14\% & 80.47\% & 0.84 \\ Pr \(\rightarrow\) Rw & Transformer & 58.23\% & 60.32\% & 48.47\% & 0.54 \\  & DANN & 67.56\% & 70.48\% & 55.12\% & 0.62 \\  & LPT & 66.45\% & 68.78\% & 54.11\% & 0.61 \\  & CDAN & 72.89\% & 75.32\% & 63.56\% & 0.69 \\  & MMTADAN & 83.42\% & 85.78\% & 78.64\% & 0.82 \\  & Transformer & 55.47\% & 57.64\% & 45.32\% & 0.51 \\  & DANN & 65.36\% & 68.12\% & 52.73\% & 0.60 \\  & LPJT & 63.42\% & 65.87\% & 50.63\% & 0.58 \\  & CDAN & 70.45\% & 73.34\% & 60.76\% & 0.66 \\  & MMTADAN & 82.57\% & 85.12\% & 75.47\% & 0.80 \\ Cl \(\rightarrow\) Ar & Transformer & 60.23\% & 63.41\% & 50.76\% & 0.57 \\  & DANN & 68.14\% & 70.23\% & 55.12\% & 0.62 \\  & LPJT & 67.35\% & 69.42\% & 52.67\% & 0.60 \\  & CDAN & 74.42\% & 76.34\% & 64.23\% & 0.69 \\  & MMTADAN & 86.24\% & 88.42\% & 80.32\% & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of various methods for the Office–Home dataset. The results clearly indicate that both the pseudo-label classifier and the feature fusion module play significant roles in enhancing the model performance. Impact of the Pseudo-Label Classifier (NLC vs. MMTADAN)  
The model's performance was markedly inferior in all tasks without the pseudo-label classifier (NLC) compared to the full MMTADAN:  
Ar \(\rightarrow\) Pr: The NLC achieved an accuracy of 75.43%, which is 10 percentage points lower than the MMTADAN's 85.67%. The F1 score also decreased by 0.13 (NLC: 0.71; MMTADAN: 0.84), underscoring the importance of the pseudo-label classifier in improving the classification performance;  
Pr \(\rightarrow\) Rw: The accuracy for the NLC was 69.23%, representing a decline of 14 percentage points compared to the MMTADAN's 83.42%. The recall difference was particularly pronounced (NLC: 58.43%; MMTADAN: 78.64%), indicating that the pseudo-label classifier aids in capturing more target domain samples, thereby enhancing the model robustness;  
Rw \(\rightarrow\) Cl: the NLC recorded an F1 score of 0.70, significantly lower than the MMTADAN's 0.80, further validating the critical role of the pseudo-label classifier in cross-domain adaptation;  
Cl \(\rightarrow\) Ar: The NLC performed the worst in this task, with an accuracy of only 58.34% and an F1 score of 0.54, both substantially below the MMTADAN's 86.24% and 0.85. This indicates that the pseudo-label classifier substantially enhances the model performance when handling images from different domains. Impact of the Feature Fusion Module (NFF vs. MMTADAN)  
The model's performance also significantly declined without the feature fusion module (NFF):  
Ar \(\rightarrow\) Pr: The NFF achieved an accuracy of just 67.54%, an 18-percentage-point drop from the MMTADAN's 85.67%. The F1 score similarly fell from 0.84 to 0.62, highlighting the critical importance of multi-scale feature fusion in enhancing the classification accuracy and robustness;  
Pr \(\rightarrow\) Rw: The NFF's accuracy was 62.45%, a 21-percentage-point reduction compared to the MMTADAN's 83.42%.