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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Vision

    Background:

    • Deep anomaly detection models often assume clean training data, learning normality from normal samples.
    • Real-world datasets frequently violate this normality assumption due to contamination, negatively impacting model performance.
    • The diverse and inconsistent nature of anomalies makes direct abnormality definition challenging.

    Purpose of the Study:

    • To propose a model-agnostic learning framework to improve deep anomaly detection by mitigating the impact of data contamination.
    • To enhance the representation of normality within deep anomaly detection models.
    • To develop a framework that is insensitive to hyperparameters and applicable to various existing methods.

    Main Methods:

    • A novel framework is proposed that identifies sample-wise normality and uses it as an iteratively updated importance weight during training.
    • The framework is designed to be model-agnostic, applicable to one-class classification, probabilistic, and reconstruction-based deep anomaly detection approaches.
    • A termination criterion inspired by the anomaly detection objective is proposed for iterative training.

    Main Results:

    • The proposed framework significantly improves the robustness of deep anomaly detection models across different contamination ratios.
    • Performance enhancements were validated on benchmark anomaly detection and image datasets.
    • The framework improved the performance of three representative deep anomaly detection methods, as measured by the area under the ROC curve.

    Conclusions:

    • The developed framework effectively reduces the gap between the normality assumption and actual training data, leading to better normality representation.
    • The approach enhances the reliability of deep anomaly detection in the presence of real-world data contamination.
    • The framework offers a versatile and effective solution for improving existing deep anomaly detection techniques without extensive parameter tuning.