# Confidence Measures for Deep Learning in Domain Adaptation
## 5 Results
### Early Stop of the GAN Training
#### 5.1.2 CIFAR \(\rightarrow\) STL  
In this experimental setup, CIFAR was used as the source domain and STL as the target domain. As in SVHN \(\rightarrow\) MNIST, the experiments were repeated 20 times to obtain more statistically reliable results. Table 3 shows the accuracy obtained using different measures as stopping criteria. Instead, Table 4 displays the mean and the standard deviation of the difference between the accuracy obtained with the use of a validation set (theoretical optimum) and the proposed measures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Max Acc.** & **Fix Iter.** & \(e\) & \(s\) & \(d_{img}\) & \(g\) & \(d_{feat}\) & \(CBP\) & \(mix_{1}\) & \(mix_{2}\) \\ \hline Acc. & 92.34\% & 91.67\% & 91.81\% & 91.79\% & 91.56\% & 91.80\% & 91.65\% & 91.66\% & **91.87\%** & 91.79\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy (Acc.) on the MNIST test set obtained stopping the training with different strategies (the experiments were repeated 20 times and the accuracies were averaged).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Max Acc.** & **Fix Iter.** & \(e\) & \(s\) & \(d_{img}\) & \(g\) & \(d_{feat}\) & \(CBP\) & \(mix_{1}\) & \(mix_{2}\) \\ \hline Mean & - & 0.67\% & 0.53\% & 0.56\% & 0.78\% & 0.54\% & 0.69\% & 0.68\% & **0.47\%** & 0.55\% \\ \hline Std & - & 0.27 & 0.29 & 0.26 & 0.47 & 0.30 & 0.34 & 0.31 & **0.21** & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean and standard deviation of the difference between the accuracy obtained with the use of a validation set and the proposed measures on the MNIST test set.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Max Acc.** & **Fix Iter.** & \(e\) & \(s\) & \(d_{img}\) & \(g\) & \(d_{feat}\) & \(CBP\) & \(mix_{1}\) & \(mix_{2}\) \\ \hline Acc. & 56.81\% & 55.90\% & 56.08\% & 56.04\% & 55.92\% & 55.94\% & 55.95\% & 56.04\% & 56.06\% & **56.18\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average accuracy (Acc.) on the STL test set obtained stopping the training with different strategies (the experiments were repeated 20 times and the accuracies were averaged).  
The experiments showed that the proposed confidence measures allow stopping the domain adaptation process when the accuracy was close to the performance that could be obtained only with a labeled validation set. Moreover, the obtained accuracy was better than the one achievable using a fixed number of iterations. It is also worth mentioning that, in our experiments, the training of the GAN tended to converge, while, in those cases when this favorable condition was not satisfied, the proposed measures might become even more effective. Furthermore, in both experimental setups, the best results were obtained using a combined measure. This suggests that different measures captured different information about the network output uncertainty. Here, measures were combined simply computing their mean (see Section 3.4), whereas it is a matter of future work to evaluate more complicated combination strategies.