Abstract
In capsule networks, the mapping of low-level
capsules to high-level capsules is achieved by a
routing-by-agreement algorithm. Since the capsule is made up of collections of neurons and the
routing mechanism involves all the capsules instead of simply discarding some of the neurons like
Max-Pooling, the capsule network has stronger representation ability than the traditional neural network. However, considering too much low-level
capsules’ information will cause its corresponding
upper layer capsules to be interfered by other irrelevant information or noise capsules. Therefore,
the original capsule network does not perform well
on complex data structure. What’s worse, computational complexity becomes a bottleneck in dealing with large data networks. In order to solve
these shortcomings, this paper proposes a group reconstruction and max-pooling residual capsule network (GRMR-CapsNet). We build a block in which
all capsules are divided into different groups and
perform group reconstruction routing algorithm to
obtain the corresponding high-level capsules. Between the lower and higher layers, Capsule MaxPooling is adopted to prevent overfitting. We conduct experiments on CIFAR-10/100 and SVHN
datasets and the results show that our method can
perform better against state-of-the-arts