针对异常检测中正常数据分布不均的问题，提出无需先验知识的加权损失函数。
Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss
- 设计重要性加权损失，自动平衡异常分数分布。
- 在三个图像和三个高光谱数据集上提升性能0.043。
- 适合真实工业场景中正常样本多样性的异常检测任务。
异常检测在工业应用中至关重要，用于识别罕见且未见过的模式以保障系统可靠性。传统模型仅基于单一类正常数据训练，在真实世界中因正常数据呈现多样化模式，导致类别不平衡和长尾异常分数分布（LTD），从而扭曲模型训练并降低检测性能，尤其对少数类实例影响显著。为此，我们提出一种专为异常检测设计的新颖重要性加权损失。相比以往分类任务中处理LTD的方法，本方法无需正常数据类别的先验知识。我们引入一种加权损失函数，结合重要性采样，使异常分数分布逼近目标高斯分布，确保正常数据的均衡表示。在三个基准图像数据集和三个真实世界高光谱成像数据集上的大量实验表明，该方法能有效缓解由LTD引起的偏差。其性能提升达0.043，凸显了在实际应用中的有效性。
原文摘要 · Abstract (English)
Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns, leading to class imbalance and long-tailed anomaly score distributions (LTD). This imbalance skews model training and degrades detection performance, especially for minority instances. To address this issue, we propose a novel importance-weighted loss designed specifically for anomaly detection. Compared to the previous method for LTD in classification, our method does not require prior knowledge of normal data classes. Instead, we introduce a weighted loss function that incorporates importance sampling to align the distribution of anomaly scores with a target Gaussian, ensuring a balanced representation of normal data. Extensive experiments on three benchmark image datasets and three real-world hyperspectral imaging datasets demonstrate the robustness of our approach in mitigating LTD-induced bias. Our method improves anomaly detection performance by 0.043, highlighting its effectiveness in real-world applications.
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