Architecture of CENN. It has a convolutional block that produces 8 feature maps. After this, the space dimensionality increases to 512 feature maps through five more convolutional blocks. These layers are connected to deconvolutional blocks that decrease the space dimensionality to one feature map in the last deconvolutional block. Fine-grained features are added from each convolution to its corresponding deconvolution.