The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk of misclassification which is usually measured by the distance between the prediction and label in a semantic hierarchical tree. In this paper, we propose to incorporate the risk-aware inter-class correlation in a discrete optimal transport (DOT) training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk. Specifically, we define the tree induced error (TIE) on a hierarchical semantic tree and extend it to its increasing function from the optimization perspective. The semantic similarity in each level of a tree is integrated with the information gain. We achieve promising results on several large scale image classification tasks with a semantic tree structure in a plug and play manner.
翻译:广泛使用的跨热带(CE)损失深度网络在分类准确性方面取得了显著进步。然而,CE损失基本上可以忽略分类错误的风险,而分类错误通常是用一个语义分层树的预测和标签之间的距离来衡量的。在本文中,我们提议通过配置其地面距离矩阵,将意识到风险的跨大西洋(CE)损失深度网络纳入一个离散的最佳运输(DOT)培训框架。地面距离矩阵可以根据等级语义风险的先验性预先定义。具体地说,我们从优化的角度界定了树在等级语义树上引起的错误(TIE),并将它扩大到其日益增强的功能。树的每层语义相似性与信息收益相结合。我们用插头和玩耍的方式,在使用语义树结构的多个大型图像分类任务上取得了可喜的成果。