Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection

2024-11-30

arXiv.org

AI Summary

Conventional wisdom holds that overfitting degrades anomaly detection performance. This work challenges that assumption, proposing a “controllable overfitting” paradigm that repurposes overfitting as a tunable mechanism for detection enhancement. Method: (1) We introduce the Aberrance Retention Quotient (ARQ) to quantify overfitting severity and identify an optimal “sweet spot”; (2) we propose the Relative Anomaly Distribution Index (RADI) as a robust, pixel-level alternative to AUROC; (3) we theoretically justify and empirically validate the general efficacy of Gaussian-noise-based pseudo-anomaly synthesis. Contribution/Results: Our approach achieves state-of-the-art performance across standard benchmarks—including MVTec AD—demonstrating for the first time that overfitting can be precisely regulated to improve both anomaly sensitivity and discriminability. This work establishes a novel modeling paradigm for unsupervised anomaly detection.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Overfitting has long been stigmatized as detrimental to model performance, especially in the context of anomaly detection. Our work challenges this conventional view by introducing a paradigm shift, recasting overfitting as a controllable and strategic mechanism for enhancing model discrimination capabilities. In this paper, we present Controllable Overfitting-based Anomaly Detection (COAD), a novel framework designed to leverage overfitting for optimized anomaly detection. We propose the Aberrance Retention Quotient (ARQ), a novel metric that systematically quantifies the extent of overfitting, enabling the identification of an optimal"golden overfitting interval."Within this interval, overfitting is leveraged to significantly amplify the model's sensitivity to anomalous patterns, while preserving generalization to normal samples. Additionally, we present the Relative Anomaly Distribution Index (RADI), an innovative metric designed to complement AUROC pixel by providing a more versatile and theoretically robust framework for assessing model performance. RADI leverages ARQ to track and evaluate how overfitting impacts anomaly detection, offering an integrated approach to understanding the relationship between overfitting dynamics and model efficacy. Our theoretical work also rigorously validates the use of Gaussian noise in pseudo anomaly synthesis, providing the foundation for its broader applicability across diverse domains. Empirical evaluations demonstrate that our controllable overfitting method not only achieves State of the Art (SOTA) performance in both one-class and multi-class anomaly detection tasks but also redefines overfitting from a modeling challenge into a powerful tool for optimizing anomaly detection.

Problem

• Leveraging controlled overfitting to improve anomaly detection sensitivity
• Introducing ARQ to quantify optimal overfitting for anomaly discrimination
• Proposing RADI to better separate normal and anomalous distributions

Innovation

• Controllable Overfitting-based Anomaly Detection (COAD) framework
• Aberrance Retention Quotient (ARQ) for optimal overfitting
• Relative Anomaly Distribution Index (RADI) for performance tracking