  This study introduces "shortcut routing," a novel routing mechanism in
capsule networks that addresses computational inefficiencies by directly
activating global capsules from local capsules, eliminating intermediate
layers. An attention-based approach with fuzzy coefficients is also explored
for improved efficiency. Experimental results on Mnist, smallnorb, and affNist
datasets show comparable classification performance, achieving accuracies of
99.52%, 93.91%, and 89.02% respectively. The proposed fuzzy-based and
attention-based routing methods significantly reduce the number of calculations
by 1.42 and 2.5 times compared to EM routing, highlighting their computational
advantages in capsule networks. These findings contribute to the advancement of
efficient and accurate hierarchical pattern representation models.
