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A new interpretable unsupervised anomaly detection method based on
  residual explanation

A new interpretable unsupervised anomaly detection method based on residual explanation

14 March 2021
David F. N. Oliveira
L. Vismari
A. M. Nascimento
J. R. de Almeida
P. Cugnasca
J. Camargo
L. Almeida
Rafael Gripp
Marcelo M. Neves
    AAML
ArXiv (abs)PDFHTML

Papers citing "A new interpretable unsupervised anomaly detection method based on residual explanation"

3 / 3 papers shown
Title
Can I trust my anomaly detection system? A case study based on
  explainable AI
Can I trust my anomaly detection system? A case study based on explainable AI
Muhammad Rashid
E. Amparore
Enrico Ferrari
Damiano Verda
71
0
0
29 Jul 2024
Large Scale Foundation Models for Intelligent Manufacturing
  Applications: A Survey
Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey
Haotian Zhang
S. D. Semujju
Zhicheng Wang
Xianwei Lv
Kang Xu
...
Jing Wu
Zhuo Long
Wensheng Liang
Xiaoguang Ma
Ruiyan Zhuang
UQCVAI4TSAI4CE
90
4
0
11 Dec 2023
Towards Meaningful Anomaly Detection: The Effect of Counterfactual
  Explanations on the Investigation of Anomalies in Multivariate Time Series
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series
Max Schemmer
Joshua Holstein
Niklas Bauer
Niklas Kühl
G. Satzger
69
2
0
07 Feb 2023
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