让深度学习模型像人一样讲道理，用对象中心思想解释图像预测
Object-Centric Neuro-Argumentative Learning
- 用对象中心学习提取图像事实，结合符号推理生成可解释结论
- 在合成数据上表现接近当前最优方法，具备可解释性优势
- 适合需要透明决策的高风险场景，如医疗或自动驾驶
近年来，随着深度学习技术被广泛应用于关键决策，其安全性、可靠性与可解释性问题日益突出。本文提出一种新型神经论证学习（Neural Argumentative Learning, NAL）架构，将基于假设的论证（Assumption-Based Argumentation, ABA）与深度学习结合，用于图像分析。该架构包含神经与符号两部分：前者通过对象中心学习对图像进行分割并编码为事实，后者利用ABA学习构建论证框架，实现基于图像的预测。在合成数据上的实验表明，NAL架构的表现可与当前最先进的方法相媲美。
原文摘要 · Abstract (English)
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.
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