Multipath feature recalibration DenseNet for image classification

Recently, deep neural networks have demonstrated their efficiency in image classification tasks, which are commonly achieved by an extended depth and width of network architecture. However, poor convergence, over-fitting and gradient disappearance might be generated with such comprehensive architectures. Therefore, DenseNet is developed to address these problems. Although DenseNet adopts bottleneck technique in DenseBlocks to avoid relearning feature-maps and decrease parameters, this operation may lead to the skip and loss of important features. Besides, it still takes oversized computational power when the depth and width of the network architecture are increased for better classification. In this paper, we propose a variate of DenseNet, named Multipath Feature Recalibration DenseNet (MFR-DenseNet), to stack convolution layers instead of adopting bottleneck for improving feature extraction. Meanwhile, we build multipath DenseBlocks with Squeeze-Excitation (SE) module to represent the interdependencies of useful feature-maps among different DenseBlocks. Experiments in CIFAR-10, CIFAR-100, MNIST and SVHN reveal the efficiency of our network, with further reduced redundancy whilst maintaining the high accuracy of DenseNet.