华东交通大学学报2024，Vol.41Issue(2)：79-86,8.
基于独立分类网络的开集识别研究
Research on Open Set Recognition Based on Independent Classification Network
摘要
Abstract
[Purpose]In order to solve the problem of image classification models lacking open set generaliza-tion due to traditional closed set training methods when facing open set recognition problems,we propose a sepa-rate independent classification network structure.[Method]Each category contains an independent linear fea-ture layer.The neural nodes designed in the feature layer can capture the category features more accurately under limited data samples.At the same time,a class of negative samples without labeling is introduced in the model training,so that the model not only relies on the feature difference of the known categories when constructing the decision boundary,but also increases the open set generalization of the model decision boundary without add-ing additional labeled samples.[Result]The results show that both the ICOR model structure and the open-set adaptive training strategy can effectively improve the OSR performance of traditional models;with the in-crease of openness,it can demonstrate better robustness;can more effectively reduce the OSR risk of the model.[Conclusion]The proposed independent classification network combined with open-set adaptive training algo-rithm has better open-set recognition performance than existing open-set recognition algorithms.关键词
深度学习/开集识别/图像分类/迁移学习Key words
deep learning/open set recognition/image classification/transfer learning分类
信息技术与安全科学引用本文复制引用
徐雪松,付瑜彬,于波..基于独立分类网络的开集识别研究[J].华东交通大学学报,2024,41(2):79-86,8.基金项目
国家自然科学基金项目(61763012) （61763012）
