arXiv Computer Vision
Sep 3

FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection

The paper introduces FuDU, a streaming active learning framework that enhances real‑time industrial defect detection by quantifying uncertainty at both image and box levels. It employs a Prototype-based Global Uncertainty Quantification module to assess image‑level uncertainty and a Dual‑entropy defect Uncertainty Evaluator for box‑level uncertainty. By fusing these uncertainties through fuzzy inference, FuDU enables expert‑knowledge‑driven adaptive sampling, improving reliability in tasks such as nuclear fuel rod defect detection.

By Zhaoyang Wang, Haiyong Chen, Binyi Su, Xinwei Lyu
arXiv AI
Aug 13

HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry

arXiv:2608. 11768v1 Announce Type: new Abstract: The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it suitable for tasks requiring transparent reasoning.

By Haoran Pei, Zhao Su, Zetao Lin, Haoran Li, Jun Shen, Qi Zhu, Lan Guo, Qingguo Zhou, Binbin Yong