Abstract
Over the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We introduce a novel Neural Argumentative Learning (NAL) architecture that integrates Assumption-Based Argumentation (ABA) with deep learning for image analysis. Our architecture consists of neural and symbolic components. The former segments and encodes images into facts using object-centric learning, while the latter applies ABA learning to develop ABA frameworks enabling predictions with images. Experiments on synthetic data show that the NAL architecture can be competitive with a state-of-the-art alternative.
Abstract (translated)
在过去十年里，随着我们越来越依赖深度学习技术来做出关键决策，关于这些技术的安全性、可靠性和可解释性的担忧也随之出现。我们提出了一种新颖的神经论证学习（NAL）架构，该架构将基于假设的论证方法（ABA）与深度学习相结合用于图像分析。我们的架构由神经和符号两个部分组成。前者使用以对象为中心的学习方式对图像进行分割并编码成事实，而后者则应用ABA学习来开发能够支持基于图像预测的ABA框架。在合成数据上的实验表明，NAL架构可以与最先进的替代方法相媲美。
URL
https://arxiv.org/abs/2506.14577
