ECLAD：利用局部聚合描述符提取概念
ECLAD: Extracting Concepts with Local Aggregated Descriptors
AI总结：
本文提出ECLAD方法，通过CNN激活图的逐像素聚合实现概念的自动提取与定位，并设计基于合成数据集的验证流程，在合成与真实数据集上均优于现有方法。
摘要：
卷积神经网络（CNN）越来越多地被用于关键系统中，在这些系统中，鲁棒性和对齐性至关重要。在此背景下，可解释人工智能领域提出了通过概念提取来生成CNN预测过程的高层解释。虽然这些方法能够检测图像中是否存在某个概念，但它们无法确定该概念的位置。此外，由于缺乏适当的验证程序，对此类方法进行公平比较十分困难。为了解决这些问题，我们提出了一种新的自动概念提取与定位方法，该方法基于通过对CNN激活图进行逐像素聚合所获得的表示。此外，我们引入了一种基于合成数据集的概念提取技术验证流程，这些合成数据集具有其主要组成部分的逐像素标注，从而减少了对人工干预的需求。在合成数据集和真实世界数据集上的大量实验表明，我们的方法优于当前最先进的替代方法。
英文摘要：
Convolutional neural networks (CNNs) are increasingly being used in critical systems, where robustness and alignment are crucial. In this context, the field of explainable artificial intelligence has proposed the generation of high-level explanations of the prediction process of CNNs through concept extraction. While these methods can detect whether or not a concept is present in an image, they are unable to determine its location. What is more, a fair comparison of such approaches is difficult due to a lack of proper validation procedures. To address these issues, we propose a novel method for automatic concept extraction and localization based on representations obtained through pixel-wise aggregations of CNN activation maps. Further, we introduce a process for the validation of concept-extraction techniques based on synthetic datasets with pixel-wise annotations of their main components, reducing the need for human intervention. Extensive experimentation on both synthetic and real-world datasets demonstrates that our method outperforms state-of-the-art alternatives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
