基于多表征学习的交叉熵集成图像分类方法OA北大核心CSTPCD

Cross-entropy is a common loss function used in classification tasks.However,existing deep classification methods often use the cross-entropy design of a single model,which leads to problems such as low generalization ability and poor robustness.Based on multi-representation learning,this study proposes a deep cross-entropy ensemble loss function to improve the generalization ability and robustness of deep networks.The proposed method learns the different representations of multiple depth perspectives under the same image data samples by constructing diversified sub-networks and finally classifies the multi-perspective representations of image data using a cross-entropy ensemble loss design.The method fully utilizes the diversified representations of multi-view deep networks for robust image classification.This means that the proposed cross-entropy losses from multiple views can be unified into an overall ensemble space for classification,thereby improving the image classification capabilities under the traditional single cross-entropy design.Experimental results on multiple image datasets such as SVHN and CIFAR show that the proposed method significantly improves the recognition accuracy compared with existing image classification methods such as MEAL and CEN.