解决长尾与噪声共存下的异常检测难题，提升模型鲁棒性。
TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection
- 分离处理长尾样本与噪声，用相似度分布预测类大小并精准采样。
- 在多个数据集上超越当前最优方法，长尾类别检测准确率显著提升。
- 适合工业缺陷检测等真实场景，对噪声和类别不均衡敏感的任务
我们针对一种实际挑战性环境下的无监督异常检测问题：正常数据集既包含缺陷区域，又具有未知的长尾产品类别分布。观察发现，现有模型存在尾部样本与噪声之间的权衡问题——若模型对像素噪声鲁棒，则在长尾样本上表现下降，反之亦然。为缓解此问题，我们独立处理长尾样本与噪声样本。为此，提出TailSampler，一种基于类间嵌入相似度分布对称性的新型类别规模预测器，可估计样本所属类别的基数，并用于专门采样长尾类样本。基于此，构建了记忆增强型异常检测模型TailedCore，其记忆模块既能有效捕捉长尾类信息，又具备抗噪能力。我们在无监督长尾噪声异常检测设置下广泛验证了TailedCore的有效性，结果表明其在多数情况下优于当前最先进方法。
原文摘要 · Abstract (English)
We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a model is robust against pixel noise, then its performance deteriorates on tail class samples, and vice versa. To mitigate the issue, we handle the tail class and noise samples independently. To this end, we propose TailSampler, a novel class size predictor that estimates the class cardinality of samples based on a symmetric assumption on the class-wise distribution of embedding similarities. TailSampler can be utilized to sample the tail class samples exclusively, allowing to handle them separately. Based on these facets, we build a memory-based anomaly detection model TailedCore, whose memory both well captures tail class information and is noise-robust. We extensively validate the effectiveness of TailedCore on the unsupervised long-tail noisy anomaly detection setting, and show that TailedCore outperforms the state-of-the-art in most settings.
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