Supervised Anomaly Detection

Supervised anomaly detection focuses on identifying unusual data points within a dataset by leveraging labeled examples of both normal and anomalous instances. Current research emphasizes improving model robustness to imbalanced datasets and unseen anomalies, exploring techniques like semi-supervised learning, generative adversarial networks, and transformer-based architectures to enhance accuracy and reduce the need for extensive labeled data. This field is crucial for various applications, including industrial quality control, fraud detection, and system security, offering significant potential for automating anomaly identification and improving decision-making in diverse domains.

Papers

May 8, 2024

Supervised Anomaly Detection for Complex Industrial Images
Aimira Baitieva, David Hurych, Victor Besnier, Olivier Bernard
Anomaly Detection Anomaly Map Industrial Datasets Anomaly Dataset Supervised Anomaly Detection Industrial Image

March 29, 2024

Long-Tailed Anomaly Detection with Learnable Class Names
Chih-Hui Ho, Kuan-Chuan Peng, Nuno Vasconcelos
Anomaly Detection Long Tailed Tail Class Supervised Anomaly Detection

January 6, 2024

Semi-supervised learning via DQN for log anomaly detection
Yingying He, Xiaobing Pei, Lihong Shen
Semi Supervised DQN Agent Log Anomaly Detection Supervised Anomaly Detection

November 20, 2023

NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
Hao Dong, Gaëtan Frusque, Yue Zhao, Eleni Chatzi, Olga Fink
Anomaly Detection Data Augmentation Method Anomaly Sample Semi Supervised Anomaly Detection Metropolis Hastings Supervised Anomaly Detection

June 6, 2023

Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer
Hanxi Li, Jingqi Wu, Hao Chen, Mingwen Wang, Chunhua Shen
Anomaly Detection Industrial Anomaly Detection Annotation Budget Efficient Anomaly Detection Patch Matching Residual Transformer Supervised Anomaly Detection AD Dataset

December 22, 2022

Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections
Yohei Baba, Takuro Hoshi, Ryosuke Mori, Gaurang Gavai
Generative Adversarial Network Anomaly Detection 3D Data Anomaly Detection Algorithm Anomalous Image Supervised Anomaly Detection Road Inspection

July 4, 2022

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun, Chongyang Zhang
Anomaly Detection Decision Boundary Anomaly Sample Supervised Anomaly Detection Boundary Aware Contrastive Learning

March 28, 2022

Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection
Choubo Ding, Guansong Pang, Chunhua Shen
Anomaly Detection Open Set Grayscale Image Anomaly Detection Datasets Supervised Anomaly Detection Anomaly Class

March 4, 2022

Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning
Soheil Esmaeilzadeh, Negin Salajegheh, Amir Ziai, Jeff Boote
Anomaly Detection Fraud Detection Abuse Detection Supervised Anomaly Detection

Supervised Anomaly Detection

Papers

Supervised Anomaly Detection for Complex Industrial Images

Long-Tailed Anomaly Detection with Learnable Class Names

Semi-supervised learning via DQN for log anomaly detection

NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation

Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer

Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection

Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning