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.

翻译：异常检测在工业应用中至关重要，用于识别罕见和未见模式以确保系统可靠性。传统模型在单一类别正常数据上训练，难以应对现实世界中正常数据呈现多样化模式的情况，导致类别不平衡和长尾异常分数分布（LTD）。这种不平衡会扭曲模型训练并降低检测性能，特别是对于少数类实例。为解决这一问题，我们提出了一种专为异常检测设计的新型重要性加权损失。与先前分类任务中处理LTD的方法相比，我们的方法无需正常数据类别的先验知识。相反，我们引入了一种结合重要性采样的加权损失函数，使异常分数分布与目标高斯分布对齐，确保正常数据的平衡表征。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，我们的方法在缓解LTD引起的偏差方面具有鲁棒性。该方法将异常检测性能提升了0.043，突显了其在实际应用中的有效性。