Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal (i.e. no hierarchy) and exclusive of one another (i.e. no overlap). CNN-based image classifiers built on this assumption, therefore, cannot take into account an innate hierarchy among target classes (e.g. cats and dogs in animal image classification) or additional information that can be easily derived from the data (e.g. numbers larger than five in the recognition of handwritten digits), thereby resulting in scalability issues when the number of target classes is large. Combining two related but slightly different ideas of hierarchical classification and logical learning by auxiliary inputs , we propose a new learning framework called hierarchical auxiliary learning , which not only address the scalability issues with a large number of classes but also could further reduce the classification/rec...

中文翻译：

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分层辅助学习

卷积神经网络（CNN）在图像分类和识别上的常规应用是基于以下假设：所有目标类别都是相等的（即没有层次结构）并且彼此排斥（即没有重叠）。因此，基于此假设的基于CNN的图像分类器不能考虑目标类别（例如动物图像分类中的猫和狗）之间的先天层次结构，也不能考虑可以轻松从数据中得出的其他信息（例如，大于5的数字）手写数字的识别），从而在目标类别数量很大时导致可伸缩性问题。通过辅助输入结合了两种相关的，但稍有不同的层次分类和逻辑学习的思想，我们提出了一种新的学习框架，称为层次辅助学习，