  In order to enhance the real-time performance of convolutional neural
networks(CNNs), more and more researchers are focusing on improving the
efficiency of CNN. Based on the analysis of some CNN architectures, such as
ResNet, DenseNet, ShuffleNet and so on, we combined their advantages and
proposed a very efficient model called Highly Efficient Networks(HENet). The
new architecture uses an unusual way to combine group convolution and channel
shuffle which was mentioned in ShuffleNet. Inspired by ResNet and DenseNet, we
also proposed a new way to use element-wise addition and concatenation
connection with each block. In order to make greater use of feature maps,
pooling operations are removed from HENet. The experiments show that our
model's efficiency is more than 1 times higher than ShuffleNet on many open
source datasets, such as CIFAR-10/100 and SVHN.
