Summary of Enhancing Anomaly Detection Via Generating Diversified and Hard-to-distinguish Synthetic Anomalies, by Hyuntae Kim et al.
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies
by Hyuntae Kim, Changhee Lee
First submitted to arxiv on: 16 Sep 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 introduces a novel unsupervised anomaly detection approach that leverages conditional perturbators and a discriminator. The perturbators generate input-dependent perturbations used to construct synthetic anomalies, while the discriminator distinguishes between normal samples and these anomalies. Two key strategies ensure generated anomalies are diverse and hard to distinguish: orthogonal perturbations and proximity constraints to normal samples. Experimental results on real-world datasets demonstrate superiority over state-of-the-art benchmarks in both image and tabular data, with adaptability to semi-supervised settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers detect unusual things (anomalies) without being told what’s normal or not. They want to learn from normal data how to spot strange things later on. Some methods try to create fake anomalies by changing normal data, but this doesn’t work well when there’s no clear pattern. To fix this, the researchers created a new method that uses special “perturbators” to make synthetic anomalies and a “discriminator” to tell them apart from normal samples. They tested it on real-world datasets and showed that it performs better than other methods in both image and table data, even when there’s some information about what’s normal or not. |
Keywords
» Artificial intelligence » Anomaly detection » Semi supervised » Unsupervised
