## 5 Experiments and Evaluation
### Performance of WS-UDA in Multi-Source/Single-Target Setting  
The performance of our proposed WS-UDA is compared to benchmark approaches for the Amazon and FDU-MTL data sets, as illustrated in Figures 3 and 4, respectively. In each figure, the target domain is represented on the \(x\)-axis, while the label prediction accuracy for WS-UDA and comparison baselines is indicated on the \(y\)-axis.  
For the Amazon data set, which comprises four domains, four separate transfer tasks are evaluated. For example, when the target unlabeled data is in the books domain, the source domains include DVD, electronics, and kitchen, resulting in the (DVD, electronics, kitchen) \(\longrightarrow\) books transfer task. This convention is consistent across all transfer tasks in the figures. Figure 3 demonstrates the performance using the Amazon data set. WS-UDA, with its weighted sequential architecture, consistently outperforms various baselines and the standalone MAN algorithm without sequential learning across all four transfer tasks. Compared to alternative baselines, WS-UDA improves the performance of individual transfer tasks by up to 3% and the overall performance of all four transfer tasks by 1.6%.  
Similar observations can be made for the FDU-MTL data set, as shown in Figure 4. WS-UDA enhances the performance of individual transfer tasks by up to 6.5% and the overall performance of all sixteen transfer tasks by 2.8%. The significance lies in the effectiveness of the weighted sequential framework's second stage, initialized by the shared feature extractor obtained from the first stage. This highlights WS-UDA's potential to improve feature discriminability, leveraging the sequential approach to simplify the learning task and generalize better to the target domain. Figure 5 depicts the average similarity between different domains. Table 2 highlights the relationship between the performance gains achieved by WS-UDA and the average similarity across domains. As the average similarity decreases, WS-UDA exhibits notable improvements in adaptation performance, showcasing  
Figure 5: Average similarity between different domains. Different target domains are represented on the \(x\)-axis. The \(y\)-axis displays the average similarity between the labeled samples of multi-source domains and the unlabeled samples from the target domain.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Target Domain** & **Improvement\%** & **Avg. Similarity** \\ \hline \multirow{4}{*}{Amazon} & Kitchen & 0.23\% & 0.9 \\  & Electronics & 0.87\% & 0.74 \\  & DVD & 1.21\% & 0.29 \\  & Books & 3.14\% & 0.2 \\ \hline \multirow{4}{*}{FDU-MTL} & Music & 0.29\% & 0.90 \\  & Books & 0.73\% & 0.87 \\  & Kitchen & 0.79\% & 0.86 \\  & Electronics & 1.13\% & 0.76 \\  & DVD & 1.16\% & 0.76 \\  & Camera & 1.22\% & 0.69 \\  & Apparel & 1.23\% & 0.69 \\  & Magazines & 1.47\% & 0.67 \\  & Health & 2.30\% & 0.57 \\  & Video & 2.88\% & 0.52 \\  & Toys & 3.20\% & 0.47 \\  & Software & 4.17\% & 0.37 \\  & MR & 5.86\% & 0.12 \\  & Sports & 6.44\% & 0.30 \\  & IMDb & 6.93\% & 0.28 \\  & Baby & 7.10\% & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: WS-UDA performance gains vs. average similarity.