# Extending Partial Domain Adaptation Algorithms to the Open-Set Setting
## 6 Results and Discussion
### Testing Cut-Off Thresholds  
Finally, we present our results for using thresholds on the obtained weights to identify probable transfer-relevant instances. Histograms of the weights are presented in Figures 5-8. The histograms suggest that the heuristic that we utilized was useful; the transfer-relevant instances (depicted in red) tended to have above-average weights, while the outlier instances (in blue) tended to have below-average weights. The separation was much more apparent in certain tasks (e.g., obstruction). We then demonstrated that a threshold on target weights could be used to select only instances that could be reliably classified from the adapted model. In practice, the threshold should be selected by using cross-validation to fit the needs of the problem at hand; in some cases, we may not mind a few mistakes if we can label more instances correctly, and in other cases, we may only want to produce labels with very high certainty.  
In addition, note that, especially when increasing the threshold above 1.0, many transfer-relevant instances were removed from the dataset, as it may be seen in Table 3. These, however, were transfer-relevant instances that did not benefit much from the adaptation. This was seen because the accuracies on subsets resulting from higher thresholds were better than the overall accuracy on transfer-relevant instances.  
For example, for the obstruction task, the adapted model had an accuracy of 76.22% on transfer-relevant instances, but after a threshold of 1.5 was imposed, the accuracy went up to 99.22%. For the rescaling task, the accuracy on the dataset with a threshold of 1.5 reaches 100%. To sum up, using our heuristic not only yielded transfer-relevant instances, but also yielded the \"best\" transfer-relevant instances in the sense that they were more likely to have benefited from the adaptation. This was not observed for the rotation task, which raises an interesting question that may be investigated in future work.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Obstruction**} & \multicolumn{6}{c}{**Displacement**} \\ \hline
**T** & **C** & **A** & **I** & **D** & **S** & **A** & **I** & **D** & **S** \\ \hline
6 & 3 & 67.7 & 69.5 & **70.9** & 61.6 & 39.0 & 41.4 & **59.8** & 39.0 \\
7 & 3 & 70.2 & 74.6 & **90.0** & 68.6 & 34.0 & 36.1 & **42.6** & 35.3 \\
5 & 4 & 75.2 & 77.3 & **85.6** & 53.2 & 79.2 & 81.3 & **82.0** & 74.4 \\
6 & 4 & 79.9 & 77.5 & **81.8** & 69.7 & 39.7 & 41.6 & **66.8** & 38.7 \\
10 & 5 & 63.1 & 67.3 & **76.2** & 57.0 & 66.6 & 73.1 & **82.3** & 70.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Rotation**} & \multicolumn{6}{c}{**Rescaling**} \\ \hline
**T** & **C** & **A** & **I** & **D** & **S** & **A** & **I** & **D** & **S** \\ \hline
3 & 3 & **66.9** & 66.7 & 64.8 & 62.1 & 77.3 & **81.3** & 81.0 & 60.6 \\
4 & 4 & 73.3 & **74.9** & 73.9 & 68.8 & 77.3 & **79.4** & 77.9 & 62.5 \\
4 & 2 & **70.0** & 65.1 & 68.9 & 65.0 & 55.8 & 67.5 & **82.7** & 69.1 \\
5 & 2 & 74.0 & 73.8 & **74.5** & 72.8 & 81.0 & 74.5 & **85.3** & 65.1 \\
6 & 2 & 66.1 & 60.1 & **67.2** & 49.3 & 72.3 & 83.7 & **94.6** & 83.4 \\
4 & 3 & 76.3 & 77.0 & **77.8** & 73.6 & 66.0 & 85.2 & **90.0** & 79.2 \\Figure 5: Obstruction task: Transfer-relevant (outlier) target instance histograms are depicted in red (blue). The figure is best viewed in color.  
Figure 6: Rotation task: Transfer-relevant (outlier) target instance histograms are depicted in red (blue). The figure is best viewed in color.  
Figure 8: Displacement task: Transfer-relevant (outlier) target instance histograms are depicted in red (blue). The figure is best viewed in color.  
Figure 7: Rescaling task: Transfer-relevant (outlier) target instance histograms are depicted in red (blue). The figure is best viewed in color.