  Capsule Networks face a critical problem in computer vision in the sense that
the image background can challenge its performance, although they learn very
well on training data. In this work, we propose to improve Capsule Networks'
architecture by replacing the Standard Convolution with a Depthwise Separable
Convolution. This new design significantly reduces the model's total parameters
while increases stability and offers competitive accuracy. In addition, the
proposed model on $64\times64$ pixel images outperforms standard models on
$32\times32$ and $64\times64$ pixel images. Moreover, we empirically evaluate
these models with Deep Learning architectures using state-of-the-art Transfer
Learning networks such as Inception V3 and MobileNet V1. The results show that
Capsule Networks can perform comparably against Deep Learning models. To the
best of our knowledge, we believe that this is the first work on the
integration of Depthwise Separable Convolution into Capsule Networks.
