Summary of Weakly Supervised Anomaly Detection Via Knowledge-data Alignment, by Haihong Zhao et al.
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
by Haihong Zhao, Chenyi Zi, Yang Liu, Chen Zhang, Yan Zhou, Jia Li
First submitted to arxiv on: 6 Feb 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 Knowledge-Data Alignment (KDAlign) framework combines rule knowledge with weakly supervised anomaly detection to improve model performance. The novel approach integrates human expert-derived rules into the knowledge space and uses Optimal Transport (OT) technique to align knowledge and data. This alignment is incorporated as an additional loss term in the objective function of Weakly Supervised Anomaly Detection (WSAD) methodologies. Experimental results on five real-world datasets demonstrate KDAlign’s superiority over state-of-the-art counterparts, achieving high performance across various anomaly types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KDAlign is a new way to improve computer systems that can detect unusual events or patterns. Right now, these systems aren’t very good at finding things they haven’t seen before because they only get a little bit of information about what’s normal and what’s not. KDAlign helps by bringing in expert knowledge from humans to help the system learn. It uses a special technique called Optimal Transport to match this human expertise with the data the system is looking at. The results show that KDAlign works much better than other methods, which means it could be very useful for things like detecting malware or finding problems in networks. |
Keywords
* Artificial intelligence * Alignment * Anomaly detection * Objective function * Supervised
