Summary of Weakly-supervised Anomaly Detection For Multimodal Data Distributions, by Xu Tan et al.
Weakly-supervised anomaly detection for multimodal data distributions
by Xu Tan, Junqi Chen, Sylwan Rahardja, Jiawei Yang, Susanto Rahardja
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Weakly-supervised Variational-mixture-model-based Anomaly Detector (WVAD) outperforms existing unsupervised methods for anomaly detection when aided by a small number of labeled anomalies. The paper addresses the limitation of current weakly-supervised methods by incorporating multimodal data distribution. WVAD consists of two components: a deep variational mixture model and an anomaly score estimator. Experimental results on three real-world datasets show the superiority of WVAD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WVAD is a new way to detect unusual patterns in data when we only have a few examples of what’s normal. Right now, computers are not very good at this task without any help from humans. But what if we could train them with just a little bit of labeled information? The WVAD algorithm does exactly that. It uses two parts: one to understand the different types of data and another to figure out how unusual something is. When tested on real-world datasets, WVAD did better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Mixture model » Supervised » Unsupervised
