Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911356255469568 |
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| author | Lee, Jungi Kim, Jungkwon Zhang, Chi Kim, Sangmin Yoo, Kwangsun Byun, Seok-Joo |
| author_facet | Lee, Jungi Kim, Jungkwon Zhang, Chi Kim, Sangmin Yoo, Kwangsun Byun, Seok-Joo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02440 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss Lee, Jungi Kim, Jungkwon Zhang, Chi Kim, Sangmin Yoo, Kwangsun Byun, Seok-Joo Machine Learning Artificial Intelligence 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. |
| title | Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.02440 |