Improvement for Convolutional Neural Networks in Image Classification Using Long Skip Connection
Hong Hai Hoang, Hoang Hieu Trinh
Abstract
In this paper, we examine and research the effect of long skip connection on convolutional neural networks (CNNs) for the tasks of image (surface defect) classification. The standard popular models only apply short skip connection inside blocks (layers with the same size). We apply the long version of residual connection on several proposed models, which aims to reuse the lost spatial knowledge from the layers close to input. For some models, Depthwise Separable Convolution is used rather than traditional convolution in order to reduce both count of parameters and floating-point operations per second (FLOPs). Comparative experiments of the newly upgraded models and some popular models have been carried out on different datasets including Bamboo strips datasets and a reduced version of ImageNet. The modified version of DenseNet 121 (we call MDenseNet 121) achieves higher validation accuracy while it has about 75% of weights and FLOPs in comparison to the original DenseNet 121.