## 5 Experiments and Evaluation
### Comparison Baselines  
Two distinct sets of experiments were conducted to validate the WS-UDA framework's efficiency. The first set of tests evaluates performance in a multi-source/single-target context, whereas the second set evaluates performance in a single-source/single-target setting.  
* Using the Amazon data set and the multi-source domain adaption configuration, we compare the results to the following baselines: 1. msDA [25]: to learn new representations for domain adaptation, it leverages marginalized stacking denoising auto-encoders. 2. DANN [41]: it proposes adversarial training as a domain adaptation representation learning approach. 3. MDAN (S-MAX) and MDAN (H-MAX) [17]: are two adversarial neural models. 4. MAN [43]: a multinomial adversarial networks. 5. DACL [57]: dual adversarial co-learning approach for text classification. Additionally, we benchmark our approach against the following studies, which contrasted their results utilizing the FDU-MTL data set with multi-source domain adaptation setting: 1. Meta [58] a meta network for storing the knowledge shared by many associated tasks.  
\begin{table}
\begin{tabular}{c c c} \hline
**WS-UDA** & **Step 1** & **Step 2** \\ \hline Learning\_rate & 0.0001 & 0.001 \\ Drop out & 0.4 & 0.3 \\ \hline Activation Function & \multicolumn{2}{c}{ReLU} \\ Optimizer & \multicolumn{2}{c}{Adam} \\ C & \multicolumn{2}{c}{128 + 64} \\ D Size & \multicolumn{2}{c}{64} \\ K & \multicolumn{2}{c}{5} \\ \hline \end{tabular}
\end{table}
Table 1: WS-UDA hyperparameters.  
* For single-source/single-target setting: using the Amazon data set, a comparison is made with the following competitors that mentioned their findings under comparable circumstance: 1. DTFC [59]: an auto-encoder-based approach for extracting complicated feature representations by disturbing distinct features with varied corruption probabilities. 2. TransNorm [60]: adversarial learning in which deep networks are trained in a conditional paradigm. 3. ALDA [46]: combines both of self-training and domain-adversarial learning in a cohesive approach for learning domain-invariant feature representations. 4. CAE [23]: an auto-encoder that integrates deep auto-encoding and causal structure-learning approaches into a single framework to generate causal representations from a single-source domain.  
To the best of our knowledge, the state-of-the-art techniques that employ the FDU-MTL data set with a single-source/single-target setting choose three domains out of the 16: MR, apparel, and baby. As a result, we report WS-UDA's performance in these three domains against the following benchmarks in order to establish a fair comparison:  
1. DANN [41]: provides comparable latent features by adding numerous regular layers as well as a new gradient reversal layer to the model.
2. DataSel [61]: depends on choosing data based on the variance in complexity and domain similarity.
3. DistanceNet [59]: adds distance metrics as a loss function to be reduced alongside the total loss function.  
Lastly, we compare our approach to the benchmarks listed below, which present their findings using the Spam data set:  
1. LDADA [62]: employs on LDA for domain adaptation.
2. JDME-G [63]: is the Joint Distribution Matching Embedding (JDME) method for projecting samples onto a latent space.