Fig. 5.

The class accuracy of a set of 100 simulated images (Fig. 4) as a function of the number of trainable parameters for the proposed MS-D network architecture and the popular U-Net architecture. For each U-Net network (U-Net-$q$), $q$ indicates the number of scaling operations used. For the MS-D architecture, results are shown for dilations $s_{ij}\in [1,10]$ (solid line) and $s_{ij}\in \{1,2,4,8,16\}$ (dashed line).