Channel-Wise and Feature-Points Reweights Densenet for Image Classification

Recent network research has demonstrated that the performance of convolutional neural networks can be improved by introducing a learning block that capture spatial correlations and channel-wise correlations . In this work, we propose a novel Channel-wise and Feature-points Reweights DenseNet (CAPR-DenseNet) architecture. The CAPR-DenseNet improves the representation power of the DenseNet by adaptively recalibrating the channel-wise feature responses and explicitly modeling the interdependencies between feature-points. First, in order to perform dynamic channel-wise feature recalibration, we construct the Channel-wise Feature Reweight DenseNet (CFR-DenseNet) by introducing the Squeeze-and-Excitation Module (SEM) to DenseNet. Then, we present a novel CAPR-DenseNet by adding a Feature-points Reweight Module (FPRM) to the CFR-DenseNet. Through massive experiments, we demonstrate that by recalibrating the channel-wise feature and the feature-points responses. Our CAPR-DenseNet performs better than DenseNet across challenging datasets CIFAR-10 and CIFAR-100.