  Deep learning is a powerful approach with good performance on many different
tasks. However, these models often require massive computational resources. It
is a worrying trend that we increasingly need models that work well on more
complex problems. In this paper, we propose and verify the effectiveness and
efficiency of SCNN, an innovative neural network inspired by the swarm concept.
In addition to introducing the relevant theories, our detailed experiments
suggest that fewer parameters may perform better than models with more
parameters. Besides, our experiments show that SCNN needs less data than
traditional models. That could be an essential hint for problems where there is
not much data.
