Research on Capsule Network Based on Attention Mechanism

The capsule network has good spatial recognition and has good accuracy in classification and recognition tasks. However, because of the dynamic routing algorithm in the capsule network, the training speed of the capsule network is slow. In order to make better use of the capsule network, reduce For its training cost, this paper proposes a capsule network based on the attention mechanism, and adds the CBAM attention module to the original capsule network to improve the network’s ability to extract information in the feature map channel and information in the feature map space, and improve the network’s learning ability, To reduce the number of network training, thereby reducing the cost of training. This paper conducts experiments based on the original neural network to verify the effectiveness and feasibility of adding the CBAM module to the capsule network. The final result is that the CBAM module can speed up the convergence speed of the capsule network by 50%.