Supervised Anomaly Detection
50 papers with code • 2 benchmarks • 3 datasets
In the training set, the amount of abnormal samples is limited and significant fewer than normal samples, producing data distributions that lead to a naturally imbalanced learning problem.
Libraries
Use these libraries to find Supervised Anomaly Detection models and implementations
MVTecAD
BTAD
CHAD
Most implemented papers
Weakly Supervised Anomaly Detection: A Survey
yzhao062/wsad
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Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
missinghwan/mgad
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To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
Daniel00008/WS-VAD-mindspore
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Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
santiagxf/ContrastiveLearning
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Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning.
Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
jx-zhong-for-academic-purpose/GCN-Anomaly-Detection
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CVPR 2019
Remarkably, we obtain the frame-level AUC score of 82. 12% on UCF-Crime.
Semi-supervised Anomaly Detection using AutoEncoders
msminhas93/anomaly-detection-using-autoencoders
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Journal of Computational Vision and Imaging Systems 2020
But for defect detection lack of availability of a large number of anomalous instances and labelled data is a problem.
Semi-supervised Anomaly Detection on Attributed Graphs
To learn node embeddings specialized for anomaly detection, in which there is a class imbalance due to the rarity of anomalies, the parameters of a GCN are trained to minimize the volume of a hypersphere that encloses the node embeddings of normal instances while embedding anomalous ones outside the hypersphere.
$\text{A}^3$: Activation Anomaly Analysis
Based on the activation values in the target network, the alarm network decides if the given sample is normal.
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score.
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
For an automated change-point-free sequence selection, the most severe 60 % of all change points (CPs) could be automatically removed with a precision of more than 0. 96 and therefore without any significant loss of training data.
