论文标题
使用卷积神经网络学习课堂层次结构
Learn Class Hierarchy using Convolutional Neural Networks
论文作者
论文摘要
关于卷积神经网络的大量研究集中在多级领域的平坦分类上。在现实世界中，许多问题自然被表达为等级分类的问题，其中要预测的类是在类别的层次结构中组织的。在本文中，我们提出了一种用于图像层次分类的新体系结构，引入了一堆具有跨透明损失函数的深线性层和中心损失的组合。所提出的体系结构可以扩展任何神经网络模型，并同时优化损失功能，以发现本地层次结构类关系和损失函数，从而从整个类层次结构中发现全局信息，同时惩罚阶级层次结构。我们在实验上表明，我们的分层分类器为在计算机视觉任务中找到应用的传统分类方法提供了优势。
A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as problems of hierarchical classification, 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 of images, introducing a stack of deep linear layers with cross-entropy loss functions and center loss combined. 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.
