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cs.LG2026
M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection
Chao Huang, Yanhui Li, Yunkang Cao +5
Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce high-confidence yet unreliable…
cs.LG2026
Noisy-Pair Robust Representation Alignment for Positive-Unlabeled Learning
Hengwei Zhao, Zhengzhong Tu, Zhuo Zheng +4
Positive-Unlabeled (PU) learning aims to train a binary classifier (positive vs. negative) where only limited positive data and abundant unlabeled data are available. While widely…