CascadeNet: Modified ResNet with Cascade Blocks

Different enhanced convolutional neural network (CNN) architectures have been proposed to surpass very deep layer bottleneck by using shortcut connections. In this paper, we present an effective deep CNN architecture modified on the typical Residual Network (ResNet), named as Cascade Network (CascadeNet), by repeating cascade building blocks. Each cascade block contains independent convolution paths to pass information in the previous layer and the middle one. This strategy exposes a concept of 'cross-passing' which differs from the ResNet that stacks simple building blocks with residual connections. Traditional residual building block do not fully utilizes the middle layer information, but the designed cascade block catches cross-passing information for more complete features. There are several characteristics with CascadeNet: Enhance feature propagation and reuse feature after each layer instead of each block. In order to verify the performance in CascadeNet, the proposed architecture is evaluated in different ways on two data sets (i.e., CIFAR-10 and HistoPhenotypes dataset), showing better results than its ResNet counterpart.