标题： 一个原型就足够了：用于可解释图像分类的单原型激活 显示英文标题

标题： One Prototype Is Enough: Single-Prototype Activation for Interpretable Image Classification

摘要： 在本文中，我们提出ProtoSolo，一种新颖的深度神经架构，用于可解释的图像分类，其灵感来自于原型网络，如ProtoPNet。现有的原型网络通常依赖于多个原型的协作决策来实现单个类别的分类和解释。相比之下，ProtoSolo只需激活一个原型即可完成分类。这使得网络可以通过仅提供与该类别原型最相似的特征来解释每个类别决策，显著降低了解释的认知复杂度。其次，我们提出了一种基于特征的比较方法，该方法使用特征图而不是全通道特征向量作为相似性比较和原型学习的对象。这种设计使ProtoSolo能够在仅依赖单个原型激活的情况下利用更丰富的全局信息进行分类。此外，我们提出了一种非原型投影学习策略，在保留原型与训练图像块之间信息关联的同时，避免了投影操作引起的网络结构剧烈变化，从而避免了其对分类性能的负面影响。在CUB-200-2011和Stanford Cars数据集上的实验表明，ProtoSolo在分类任务中表现出色，并且在解释的认知复杂度方面达到了最先进的可解释方法的最佳水平。代码可在https://github.com/pyt19/ProtoSolo获取。 显示英文摘要

摘要： In this paper, we propose ProtoSolo, a novel deep neural architecture for interpretable image classification inspired by prototypical networks such as ProtoPNet. Existing prototype networks usually rely on the collaborative decision-making of multiple prototypes to achieve the classification and interpretation of a single category. In contrast, ProtoSolo only requires the activation of a single prototype to complete the classification. This allows the network to explain each category decision by only providing the features that are most similar to the prototype of that category, significantly reducing the cognitive complexity of the explanation. Secondly, we propose a feature-based comparison method, which uses feature map instead of full-channel feature vector as the object of similarity comparison and prototype learning. This design enables ProtoSolo to utilize richer global information for classification while relying on a single prototype activation. In addition, we propose a non-prototype projection learning strategy, which preserves the information association between the prototype and the training image patches while avoiding the sharp change of the network structure caused by the projection operation, thus avoiding its negative impact on the classification performance. Experiments on the CUB-200-2011 and Stanford Cars datasets show that ProtoSolo achieves superior performance in classification tasks and reaches the best level in terms of cognitive complexity of explanations compared to state-of-the-art interpretable methods. The code is available at https://github.com/pyt19/ProtoSolo.