## 4 Experiments
### Quantitative Evaluation  
Tables 1-3 show quantitative evaluation results on the simulated biomedical test set, which we will detailedly discuss in the following three subsections. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data Set** & \multicolumn{5}{c}{**SNR = 0.5**} & \multicolumn{5}{c}{**SNR = 1.5**} \\ \hline
**MethodMetric** & \(\varepsilon\) & \(F\) & \(E\) & \(S\) & \(\varepsilon\) & \(F\) & \(E\) & \(S\) \\ \hline Itti [30] & 0.1277 & 0.4759 & 0.3811 & 0.4445 & 0.1206 & 0.6396 & 0.4639 & 0.4781 \\ LC [31] & 0.1626 & 0.3277 & 0.4466 & 0.4846 & 0.1463 & 0.4615 & 0.4369 & 0.5022 \\ SR [32] & 0.1340 & 0.2535 & 0.3020 & 0.4406 & 0.1316 & 0.3439 & 0.2911 & 0.4423 \\ \hline IG [33] & 0.2843 & 0.1713 & 0.4775 & 0.4262 & 0.2978 & 0.1848 & 0.4739 & 0.4322 \\ SIG [34] & 0.2623 & 0.2647 & 0.4959 & 0.4781 & 0.2310 & 0.3387 & 0.5134 & 0.5177 \\ VA [35] & 0.2843 & 0.1713 & 0.4775 & 0.4262 & 0.2978 & 0.1848 & 0.4739 & 0.4322 \\ SVA [34] & 0.2625 & 0.2647 & 0.4957 & 0.4779 & 0.2305 & 0.3414 & 0.5129 & 0.5186 \\ VBP [36] & 0.1295 & 0.3049 & 0.4033 & 0.4527 & 0.1224 & 0.4588 & 0.4053 & 0.4717 \\ SalGAN [37] & 0.1427 & 0.1984 & 0.3126 & 0.4411 & 0.1585 & 0.2367 & 0.4090 & 0.4629 \\ \hline PUB-SalNet & **0.0914** & **0.6573** & **0.7036** & **0.6494** & **0.0762** & **0.7426** & **0.7522** & **0.7209** \\ \hline Improvement & \(\downarrow\) 28.43\% & \(\uparrow\) 38.12\% & \(\uparrow\) 41.88\% & \(\uparrow\) 34.01\% & \(\downarrow\) 36.82\% & \(\uparrow\) 16.10\% & \(\uparrow\) 46.51\% & \(\uparrow\) 39.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of performance of ten unsupervised methods with four metrics on the simulated biomedical test sets. \(\varepsilon\) stands for Mean Absolute Error (MAE), \(F\) for region similarity, \(E\) for the enhanced alignment measure, and \(S\) for structural similarity. Lower is better for \(\varepsilon\), and higher is better for the other three metrics. The results are calculated according to Equations (6)–(8). The best performance of each metric is in **bold** and the second best is underlined. The improvements of our PUB-SalNet over the best of other methods in relative percentage is shown in the last row. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data Set** & \multicolumn{5}{c}{**SNR = 0.5**} & \multicolumn{5}{c}{**SNR = 1.5**} \\ \hline
**MethodMetric** & \(\varepsilon\) & \(F\) & \(E\) & \(S\) & \(\varepsilon\) & \(F\) & \(E\) & \(S\) \\ \hline B & 0.1461 & 0.1628 & 0.3692 & 0.4230 & 0.1433 & 0.1628 & 0.3960 & 0.4223 \\ U+B & 0.2870 & 0.1628 & 0.4834 & 0.3585 & 0.2677 & 0.1628 & 0.5130 & 0.3693 \\ P+B & 0.1063 & 0.5631 & 0.5906 & 0.5661 & 0.0949 & 0.6551 & 0.5947 & 0.5979 \\ P+U & 0.1104 & 0.6214 & 0.6306 & **0.6506** & 0.0973 & **0.7544** & 0.7465 & **0.7617** \\ P+U+B & **0.0914** & **0.6573** & **0.7036** & 0.6494 & **0.0762** & 0.7426 & **0.7522** & 0.7209 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparisons between different combination of modules from our PUB-SalNet model. **B** stands for a single unsupervised backpropagation module; **U+B** stands for U-SalNet architecture with **B**; **P+B** means **B** based on the pre-training method; **P+U** means U-SalNet based on the pre-training method, note that this is actually not an unsupervised method; **P+U+B** is our proposed PUB-SalNet.