Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection 事件

Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection arXiv:2412.15668v2 Announce Type: replace Abstract: This paper focuses on a significant yet challenging task: out-of-distribution detection (OOD detection), which aims to distinguish and reject test samples with semantic shifts, so as to prevent models trained on in-distribution (ID) data from producing unreliable predictions. Although previous works have made decent success, they are ineffective for real-world