Summary of Friend or Foe? Harnessing Controllable Overfitting For Anomaly Detection, by Long Qian and Bingke Zhu and Yingying Chen and Ming Tang and Jinqiao Wang
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection
by Long Qian, Bingke Zhu, Yingying Chen, Ming Tang, Jinqiao Wang
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper challenges the conventional wisdom that overfitting is detrimental to model performance, particularly in anomaly detection. Instead, it proposes Controllable Overfitting-based Anomaly Detection (COAD), which leverages overfitting to enhance model discrimination capabilities. The authors introduce two novel metrics: Aberrance Retention Quotient (ARQ) and Relative Anomaly Distribution Index (RADI). ARQ quantifies the extent of overfitting, enabling the identification of an optimal “golden overfitting interval” where overfitting amplifies the model’s sensitivity to anomalous patterns. RADI tracks how overfitting impacts anomaly detection, offering a theoretically robust framework for assessing model performance. Empirical evaluations demonstrate that COAD achieves State of the Art (SOTA) performance in both one-class and multi-class anomaly detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a new look at overfitting in anomaly detection. Instead of seeing it as a problem, it shows how to control overfitting to make models better at finding anomalies. The authors create a special framework called COAD that uses overfitting to help models detect anomalies more accurately. They also introduce two new ways to measure how well a model is doing: ARQ and RADI. These metrics help us understand how overfitting affects anomaly detection and show that this approach can be really effective. The results are impressive, with the COAD method achieving the best performance in many cases. |
Keywords
» Artificial intelligence » Anomaly detection » Overfitting
