Summary of Statistical Test For Anomaly Detections by Variational Auto-encoders, By Daiki Miwa et al.
Statistical Test for Anomaly Detections by Variational Auto-Encoders
by Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy, Teruyuki Katsuoka, Ichiro Takeuchi
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 study proposes the VAE-AD Test as a method for quantifying the statistical reliability of Variational Autoencoder (VAE)-based anomaly detection (AD) within the framework of statistical testing. The VAE-AD Test uses p-values to quantify the reliability of detected anomalies, allowing for controlled false detection probabilities. This approach is theoretically guaranteed in finite samples due to its foundation in selective inference. Numerical experiments on artificial data and brain image analysis demonstrate the validity and effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us make sure that when we use special computer programs to find unusual patterns (anomalies) in things like medical images, those anomalies are really there and not just a mistake. They propose a new way to test these programs, called VAE-AD Test, which gives us a number that shows how likely it is that the anomaly isn’t actually there. This can help us be more confident when we’re making important decisions based on these results. |
Keywords
* Artificial intelligence * Anomaly detection * Inference * Variational autoencoder
