做异常检测或弱监督学习的团队终于有了统一评估工具——WSADBench用70万次实验戳破了三个方向各自为战的假象，看完你会重新思考该用专用算法还是通用模型。
弱监督异常检测（WSAD）长期分为不完整、不精确和不准确三种监督方向，但缺乏统一评估框架。该论文提出WSADBench，首个跨场景统一基准，系统评估了36种算法在4种模态下的表现，基于超过70万次实验。研究发现：弱监督场景间存在强相关性，挑战了当前研究方向的孤立性；专用WSAD算法仅在极端标签稀缺时占优，随监督增强或面对分布外数据时，表格基础模型和通用分类方法迅速超越；无标签数据在不同设置下效用不一致，相比标签精炼收益有限；模型对不同类型标签噪声的敏感性不对称。该基准已开源，旨在推动WSAD研究。
Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whether they address unique challenges or share fundamental mechanics. This paper introduces WSADBench, the first benchmark that unifies evaluation across distinct weakly supervised scenarios, benchmarking diverse approaches from specialized WSAD methods to advanced tabular foundation models. WSADBench establishes standardized protocols to evaluate 36 algorithms across 4 modalities by systematically varying label quantity, granularity, and quality, revealing the performance boundaries of various methods. Based on over 700K experiments, WSADBench reveals four critical insights: (i) Strong intrinsic correlations exist between these weak supervision scenarios, challenging the isolation of current research directions. (ii) Specialized WSAD algorithms excel only in extreme label-scarcity regimes but are quickly dominated by tabular foundation models and general classification methods as supervision increases or in OOD scenarios. (iii) Unlabeled data shows inconsistent utility across settings, with marginal gains compared to label refinement. (iv) Models exhibit asymmetric sensitivity to different types of label noise. We release WSADBench as an open-source benchmark with code and datasets to facilitate future WSAD research: https://github.com/SUFE-AILAB/WSADBench.