Summary of Uncertainty-aware Evaluation Of Auxiliary Anomalies with the Expected Anomaly Posterior, by Lorenzo Perini et al.
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
by Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu
First submitted to arxiv on: 22 May 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 The paper proposes a novel approach to quantify the quality of auxiliary synthetic anomalies used for training anomaly detectors. Anomaly detection is the task of identifying rare and unexpected events, which can be challenging to collect in real-world applications. Existing methods often rely on poor-quality synthetic anomalies that may deteriorate the detector’s performance. The authors introduce the expected anomaly posterior (EAP), a score function based on uncertainty measures, to assess the quality of auxiliary anomalies. Experimental results on 40 benchmark datasets demonstrate that EAP outperforms 12 adapted data quality estimators in most cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a computer to spot weird things in pictures or data. But it’s hard to find lots of examples of what’s “weird” because they’re rare and unexpected. To solve this problem, scientists use fake “anomalies” to help train the computer. The issue is that these fake anomalies might not be very good and could even make the computer worse at spotting real weird things. The paper proposes a new way to measure how good or bad these fake anomalies are. They tested it on lots of different kinds of data and found that their method works better than others in most cases. |
Keywords
» Artificial intelligence » Anomaly detection
