提出可解释3D点云分类器的最小充分组合，高效且精准。
MiSS: A Logic-Driven Explanation of Minimal Sufficient Coalitions for Point Cloud Classifiers
- 通过扰动相对充分性推理，用加权MaxSAT生成候选组合。
- 在ModelNet40和ShapeNet上精度与覆盖度优于基线，解释速度更快。
- 无需白盒逻辑编码，适合部署于黑箱分类器的可解释场景。
我们提出MiSS，一种基于查询的黑箱框架，通过扰动相对充分性推理解释3D点云分类器。MiSS将超点划分视为可解释抽象层，询问在特定扰动分布下，能否从几何区域的最小组合中认证原始预测。不同于需布尔特征空间或白盒逻辑编码的反向解释方法，MiSS分离候选生成与验证：使用带启发式基数下限、精确大小回退、安全收紧上界、阻塞子句及从先前查询学习的代理获取启发式，通过加权MaxSAT生成候选组合；再由黑箱统计预言机通过预测查询判断充分性。系统返回经统计验证的充分组合作为二值归因，当认证搜索完成时保证最小基数。在ModelNet40和ShapeNet上的实验表明，对PointNet和PointMLP分类器，在多数设置下精度与覆盖度高于规则基线，且解释时间低于穷举搜索。
原文摘要 · Abstract (English)
We present MiSS, a black-box, query-based framework for explaining 3D point cloud classifiers through perturbation-relative sufficiency reasoning. MiSS treats a superpoint partition as an interpretable abstraction layer and asks whether the original prediction can be certified from a minimal coalition of geometric regions under a specified perturbation distribution. Unlike abductive explainers that require Boolean feature spaces or white-box logical encodings of the predictor, MiSS separates candidate proposal from verification: a weighted MaxSAT procedure proposes coalitions using a heuristic adaptive cardinality floor, certified exact-size fallback, a safely tightened upper bound, blocking clauses, and a surrogate acquisition heuristic learned from previous oracle evaluations, while a blackbox statistical oracle decides sufficiency from prediction queries. The system returns a statistically verified sufficient coalition as a binary attribution, with minimum cardinality guaranteed when certified search completes. Experiments on ModelNet40 and ShapeNet with PointNet and PointMLP classifiers show higher precision and coverage than rule-based baselines in most settings, with lower explanation time than exhaustive search.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。