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Summary of Research and Application Of Transformer Based Anomaly Detection Model: a Literature Review, by Mingrui Ma et al.


Research and application of Transformer based anomaly detection model: A literature review

by Mingrui Ma, Lansheng Han, Chunjie Zhou

First submitted to arxiv on: 14 Feb 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 reviews the application of Transformer models in anomaly detection, a crucial task in Natural Language Processing (NLP). The authors highlight the current challenges in anomaly detection and delve into the operating principles of Transformer and its variants. They also discuss various scenarios for applying Transformer-based anomaly detection models, including datasets and evaluation metrics used. Furthermore, the review emphasizes key challenges and future research trends in this domain. The paper includes a comprehensive compilation of over 100 core references related to Transformer-based anomaly detection.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper looks at how the Transformer model can be used to find unusual patterns in language. This is important because it can help us understand what makes certain things normal or abnormal. The authors explain how the Transformer works and show its strengths and weaknesses. They also talk about different ways this technology could be used, such as identifying fake news or detecting errors in text. Finally, they highlight some challenges that need to be addressed to make this technology even better.

Keywords

* Artificial intelligence  * Anomaly detection  * Natural language processing  * Nlp  * Transformer