This paper addresses the challenge of fully unsupervised image anomaly detection (FUIAD), where training data may contain unlabeled anomalies. Conventional methods assume anomaly-free training data, but real-world contamination leads models to absorb anomalies as normal, degrading detection performance. To mitigate this, we propose a two-stage framework that systematically exploits inherent learning bias in models. The learning bias stems from: (1) the statistical dominance of normal samples, driving models to prioritize learning stable normal patterns over sparse anomalies, and (2) feature-space divergence, where normal data exhibit high intra-class consistency while anomalies display high diversity, leading to unstable model responses. Leveraging the learning bias, stage 1 partitions the training set into subsets, trains sub-models, and aggregates cross-model anomaly scores to filter a purified dataset. Stage 2 trains the final detector on this dataset. Experiments on the Real-IAD benchmark demonstrate superior anomaly detection and localization performance under different noise conditions. Ablation studies further validate the framework's contamination resilience, emphasizing the critical role of learning bias exploitation. The model-agnostic design ensures compatibility with diverse unsupervised backbones, offering a practical solution for real-world scenarios with imperfect training data. Code is available at https://github.com/hustzhangyuxin/LLBNAD.

翻译：本文针对完全无监督图像异常检测（FUIAD）的挑战展开研究，该场景下训练数据可能包含未标记的异常样本。传统方法假设训练数据完全无异常，但现实场景中的数据污染会导致模型将异常吸收为正常模式，从而降低检测性能。为缓解此问题，我们提出一种两阶段框架，系统性地利用模型中固有的学习偏差。该学习偏差源于两方面：（1）正常样本的统计主导性促使模型优先学习稳定的正常模式而非稀疏的异常；（2）特征空间发散性，即正常数据具有较高的类内一致性，而异常样本呈现高度多样性，导致模型响应不稳定。基于此学习偏差，第一阶段将训练集划分为子集，训练子模型并通过聚合跨模型异常评分来筛选净化数据集。第二阶段在此净化数据集上训练最终检测器。在Real-IAD基准测试上的实验表明，该方法在不同噪声条件下均实现了优异的异常检测与定位性能。消融研究进一步验证了框架对数据污染的鲁棒性，凸显了利用学习偏差的关键作用。该框架采用模型无关设计，可兼容多种无监督骨干网络，为训练数据不完美的实际场景提供了实用解决方案。代码发布于https://github.com/hustzhangyuxin/LLBNAD。