Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss

• URL: http://arxiv.org/abs/2601.02440v1
• Date: Mon, 05 Jan 2026 10:02:09 GMT
• Title: Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss
• Authors: Jungi Lee, Jungkwon Kim, Chi Zhang, Sangmin Kim, Kwangsun Yoo, Seok-Joo Byun,
• Abstract summary: Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability.<n>Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns.<n>We propose a novel importance-weighted loss designed specifically for anomaly detection.<n>Our method improves anomaly detection performance by 0.043, highlighting its effectiveness in real-world applications.
• Score: 7.364074727181891
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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