In this paper, we hypothesize that the effects of the degree of typicality in natural semantic categories can be generated based on the structure of artificial categories learned with deep learning models. Motivated by the human approach to representing natural semantic categories and based on the Prototype Theory foundations, we propose a novel Computational Prototype Model (CPM) to represent the internal structure of semantic categories. Unlike other prototype learning approaches, our mathematical framework proposes a first approach to provide deep neural networks with the ability to model abstract semantic concepts such as category central semantic meaning, typicality degree of an object's image, and family resemblance relationship. We proposed several methodologies based on the typicality's concept to evaluate our CPM-model in image semantic processing tasks such as image classification, a global semantic description, and transfer learning. Our experiments on different image datasets, such as ImageNet and Coco, showed that our approach might be an admissible proposition in the effort to endow machines with greater power of abstraction for the semantic representation of objects' categories.
翻译:在本文中,我们假设自然语义类别典型程度的影响可以基于深层次学习模型所学人造类别的结构产生。受人类代表自然语义类别的方法的驱动,并基于原型理论基础,我们提出了一个新的计算模型(CPM),以代表语义类别内部结构。与其他原型学习方法不同,我们的数学框架提出了一种第一种方法,为深层神经网络提供能够模拟抽象语义概念的能力,例如类中央语义概念、物体图像的典型程度和家庭相似关系等。我们根据典型概念提出了几种方法,用以评估我们的CPM模型的图像语义处理任务,如图像分类、全球语义描述和传输学习。我们对不同图像数据集的实验,如图象网和科科,表明我们的方法或许是一种可以接受的建议,在努力中,为对象类别的语义表达提供更大的抽象能力。