Spatial oblivion channel attention targeting intra-class diversity feature learning

Abstract

Convolutional neural networks (CNNs) have successfully driven many visual recognition tasks including image classification. However, when dealing with classification tasks with intra-class sample style diversity, the network tends to be disturbed by more diverse features, resulting in limited feature learning. In this article, a spatial oblivion channel attention (SOCA) for intra-class diversity feature learning is proposed. Specifically, SOCA performs spatial structure oblivion in a progressive regularization for each channel after convolution, so that the network is not restricted to a limited feature learning, and pays attention to more regionally detailed features. Further, SOCA reassigns channel weights in the progressively oblivious feature space from top to bottom along the channel direction, to ensure the network learns more image details in an orderly manner while not falling into feature redundancy. Experiments are conducted on the standard classification dataset CIFAR-10/100 and two garbage datasets with intra-class diverse styles. SOCA improves SqueezeNet, MobileNet, BN-VGG-19, Inception and ResNet-50 in classification accuracy by 1.31%, 1.18%, 1.57%, 2.09% and 2.27% on average, respectively. The feasibility and effectiveness of intra-class diversity feature learning in SOCA-enhanced networks are verified. Besides, the class activation map shows that more local detail feature regions are activated by adding the SOCA module, which also demonstrates the interpretability of the method for intra-class diversity feature learning.