传统的岩石薄片图像分类依赖于大量人工标记的图像样本,这种方式受制于标记人员的经验和能力,且无法通过不断增加的未标记岩石薄片图像样本实现分类能力的可扩展式增强.该文提出的在平均教师(mean teacher,MT)模型的基础上,通过在无监督损失中添加层一致性正则化项的方式约束师生模型的层次结构,实现对未标记数据信息的有效利用.消融实验和层一致性平均教师(hierarchy consistency mean teacher,HCMT)模型对比实验结果表明,层一致性正则化方法利用了未标记数据的有效信息,提升了 MT模型的分类效果,使得HCMT模型可以在半标记数据集中获得如全标记数据集相似的分类能力.该实验表明,半监督学习模型利用大量未标记岩石薄片图像数据可以提升模型分类的能力.
Semi-supervised Rock Slice Image Classification Based on Hierarchy Consistency Mean Teacher Model
Traditional rock slice image classification relies on a large number of manually labeled image samples,which is subject to the experience and ability of the labelers.This practice limits the scalability of classification enhancement as increasing unlabeled rock slice image samples does not contribute effectively.In order to achieve effective utilization of unlabeled data information,the hierarchy consistency mean teacher(HCMT)model adds a hierarchy consistency regularization term to the unsupervised loss of the mean teacher(MT)model to constrain the hierarchical structure of the teacher-student model.Ablation experiments and comparative analyses reveal that the introduction of hierarchy consistency regularization method improves the classification performance of the MT model by using the effective information of unlabeled data.As a result,the HCMT model achieves comparable classification capability in both half-labeled and fully labeled dataset.The experiments show the potential of the semi-supervised learning model to improve the classification ability of the model by using a large number of unlabeled rock slice image data.
semi-supervised learningmean teacher(MT)modelclassification of rock slice imageshierarchy consistency method
万方数据
维普
