Feature Reinforcement Network for Image Classification

Deep Learning has attracted much attention these years as it produces fabulous performance in various applications. Most researchers have mainly focused on improving and optimizing the network structure, e.g., deeper and deeper networks are constructed to extract high-level features from raw data. In this paper, we propose a two-wing deep convolutional network, called Feature Reinforcement Networks (FRN). One wing acts as the traditional operation in VGG, ResNet and DenseNet, while the other wing called feature reinforcement block (FRB) also conducts layer-wise convolution operations which share the convolution parameters of the former layer. Then, Relu function is employed in FRB to rectify the feature maps except the output layer. The outputs of these wings are integrated as the input of the next convolution layer. It is confirmed that the proposed FRN is more sensitive to the informative features. Our experiments on a few multimedia datasets prove FRN outperforms the original deep neural networks.