Summary of Exploiting Autoencoder’s Weakness to Generate Pseudo Anomalies, by Marcella Astrid et al.
Exploiting Autoencoder’s Weakness to Generate Pseudo Anomalies
by Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, Seung-Ik Lee
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a novel approach to improve the performance of autoencoders (AEs) in anomaly detection tasks. By exploiting the weakness of AEs in reconstructing anomalies too well, researchers create pseudo-anomalies by adding adaptive noise to normal data. This technique enhances the discriminative capability of AEs for detecting anomalies, as demonstrated through extensive experiments on various datasets such as Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better detect unusual events that don’t follow typical patterns. The problem is that normal data can sometimes be reconstructed really well by computers, even when it’s supposed to fail. To fix this, scientists come up with a new way to create fake anomalies from normal data and noise. This makes it easier for computers to tell apart real anomalies from normal things. They tested their idea on lots of different datasets and showed that it works pretty well. |
Keywords
» Artificial intelligence » Anomaly detection
