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.

在本文中，我们假设可以基于深度学习模型学习的人工类别的结构来生成自然语义类别中典型程度的影响。受人类表示自然语义类别的方法的启发，并基于原型理论基础，我们提出了一种新颖的计算原型模型（CPM）来表示语义类别的内部结构。与其他原型学习方法不同，我们的数学框架提出了第一种方法，为深度神经网络提供建模抽象语义概念的能力，例如类别中心语义、对象图像的典型程度和家庭相似关系。我们根据典型性提出了几种方法论” s 概念在图像语义处理任务中评估我们的 CPM 模型，例如图像分类、全局语义描述和迁移学习。我们在不同图像数据集（如 ImageNet 和 Coco）上的实验表明，我们的方法可能是一个可接受的命题，旨在赋予机器对对象类别的语义表示具有更大的抽象能力。