Research on adaptive local feature enhancement in convolutional neural networks

Abstract

Local feature extraction is one of the key characteristics of convolutional neural networks (CNNs). This study proposes an adaptive local feature enhancement (ALFE) model with a low-frequency general appearance-enhancement operator and a high-frequency local detail enhancement operator to improve local features of CNNs. Through supervised training, the model could adaptively adjust enhancement parameters and achieve a global-local enhancement of training images and CNNs. The performance of ALFE was first preliminarily evaluated with a self-built CNN on CIFAR-10 data set in different conditions of image augmentation and feature pooling. CNNs with ALFE could increase the top-1 accuracy compared against CNNs in the same conditions, and nearly reach the same level of performance for different pooling approaches. With only two extra adjustable parameters, this model could effectively avoid overfitting, without affecting the convergence speed of CNN. In addition, the extra burden of network complexity could be neglected. Further, experiments of three existing popular CNNs (AlexNet, VGGNet and ResNet) with ALFE were carried out on dogs versus cats, Tiny ImageNet, and SVHN data sets, respectively. The results show that ALFE is feasible for the existing popular CNN models, improving their top-1 accuracies without changing their convergence speeds.