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

【目的】为解决图像分类模型面对传统闭集训练方式出现的模型缺乏开集泛化性的问题,提出了一种分离式的独立分类网络结构。【方法】每个类别都包含独立的线性特征层,特征层中设计的神经元节点能够在有限的数据样本下更准确地捕获类别特征。同时,在模型训练时,文中引入了一类无需标注的负样本,使得模型在构建决策边界时不仅依赖于已知类别的特征差异,在不增加额外标注样本的情况下,增加模型决策边界的开集泛化性。【结果】结果表明:独立分类网络开集识别(ICOR)模型结构和开集自适应训练策略均能有效改善传统模型开放集识别(OSR)性能;随着开放度的增加,能表现出更好的鲁棒性,能更有效地降低模型的OSR风险。【结论】提出的独立分类网络并融合开集自适应训练的算法比现有开集识别算法具有更优的开集识别性能。

【Purpose】In order to solve the problem of image classification models lacking open set generalization due to traditional closed set training methods when facing open set recognition problems,we propose a separate independent classification network structure.【Method】Each category contains an independent linear feature 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 adding 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 increase 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 algorithm has better open-set recognition performance than existing open-set recognition algorithms.