A large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as hierarchical classification problems, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification, introducing a stack of deep linear layers using cross-entropy loss functions combined to a center loss function. The proposed architecture can extend any neural network model and simultaneously optimizes loss functions to discover local hierarchical class relationships and a loss function to discover global information from the whole class hierarchy while penalizing class hierarchy violations. We experimentally show that our hierarchical classifier presents advantages to the traditional classification approaches finding application in computer vision tasks. The same approach can also be applied to some CNN for text classification.

关于卷积神经网络（CNN）的大量研究都集中在多类领域中的扁平分类上。在现实世界中，许多问题自然地表示为层次分类问题，其中要预测的类别按类别层次进行组织。在本文中，我们提出了一种用于分层分类的新架构，引入了使用交叉熵损失函数与中心损失函数相结合的深层线性层的堆栈。所提出的体系结构可以扩展任何神经网络模型，并同时优化损失函数以发现局部层次的类关系，以及损失函数以从整个类层次结构中发现全局信息，同时惩罚类层次结构违规。我们通过实验表明，我们的分层分类器为传统分类方法提供了优势，可在计算机视觉任务中找到应用。相同的方法也可以应用于某些CNN进行文本分类。