# Deep Ensembles Based on Stochastic Activations for Semantic Segmentation
## 4 Result on Skin Detection
### Experiments  
Table 6 reports the F1-score obtained on the 11 testing sets by the networks and ensemble proposed in this paper compared to some state-of-the-art approaches obtained in each test set. Moreover, the average F1-score and the rank of the method with respect to the average value were calculated. The results of approaches followed by a citation were taken from the related papers and for HarDNet were calculated using the same parameter configuration used for the polyp dataset [23] (a loss function that is a weighted sum of binary cross-entropy and IoU, an Adam optimizer with a learning rate of 0.001 and 100 epochs). As far as our methods are concerned, to avoid overfitting, we maintained for the training on skin detection the same parameter configuration described above for polyp segmentation, including data augmentation. Due to this configuration, the results are quite different from those published in [22] for the same network, but the aim, in this case, was to validate our ensemble without ad hoc tuning per dataset. Compared with the state-of-the-art results in [22] and [37] (only the best method are reported for sake of space), the proposed ensemble resnet50-352_sel gets the best average performance. Notice that in [22] DeepLabV3+ was compared with several other skin detector methods, reporting the best performance. This is a valuable result since it proves that the good performance reported for the previous problem can be replicated in a very different context. As a final experiment, we studied the performance of ensemble methods by varying the number of networks in the ensemble and the number of training epochs. For the sake of computation time, we considered only resnet50-352_relu vs. resnet50-352_sto, with the aim of studying the effects of stochastic activation selection in the design of ensembles. The graphs in Figure 2 report the performance obtained in the ECU testing set by ensembles of different sizes (each ensemble was obtained by adding a new network to the previous one, \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **FV** & **Prat** & **MCG** & **UC** & **CMQ** & **SFA** & **HGR** & **Sch** & **VMD** & **ECU** & **VT** & **Avg** & **Rank** \\ \hline resnet50-224 & 0.694 & 0.874 & 0.862 & 0.866 & 0.797 & 0.939 & 0.954 & 0.760 & 0.608 & 0.927 & 0.682 & 0.815 & 7 \\ resnet50-352 & 0.745 & 0.910 & 0.880 & 0.881 & 0.831 & 0.948 & 0.962 & 0.784 & 0.727 & 0.945 & 0.742 & 0.850 & 4 \\ HarDNet-224 & 0.674 & 0.890 & 0.882 & 0.894 & 0.819 & 0.949 & 0.963 & 0.792 & 0.677 & 0.936 & **0.756** & 0.839 & 3 \\ HarDNet-352 & 0.667 & 0.913 & **0.887** & 0.902 & 0.835 & **0.952** & **0.968** & **0.795** & 0.729 & 0.946 & 0.744 & 0.849 & 2 \\ resnet50-352\_sel & 0.742 & **0.917** & 0.884 & **0.910** & **0.840** & **0.952** & **0.968** & 0.785 & **0.742** & **0.949** & 0.755 & **0.859** & **1** \\ FusAct3 [39] & 0.790 & 0.874 & 0.884 & 0.896 & 0.825 & 0.951 & 0.961 & 0.776 & 0.669 & 0.933 & 0.737 & 0.845 & 6 \\ FusAct10 [39] & 0.796 & 0.864 & 0.884 & 0.899 & 0.821 & 0.951 & 0.959 & 0.776 & 0.671 & 0.929 & 0.748 & 0.845 & 5 \\ SegNet [22] & 0.717 & 0.730 & 0.813 & 0.802 & 0.737 & 0.889 & 0.869 & 0.708 & 0.328 & - & - & - & \\ U-Net [22] & 0.576 & 0.787 & 0.779 & 0.713 & 0.686 & 0.848 & 0.836 & 0.671 & 0.332 & - & - & - & \\ DeepLab [22] & **0.771** & 0.875 & 0.879 & 0.899 & 0.817 & 0.939 & 0.954 & 0.774 & 0.628 & - & - & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experiments on skin datasets (F1-score). The last two columns report the average F1-score on all the tested datasets and the rank of Avg (best results in bold).