Anomaly Detection Model

Anomaly detection models aim to identify unusual patterns or outliers in data, crucial for various applications like cybersecurity and predictive maintenance. Current research emphasizes improving model robustness across diverse data types (e.g., time series, images, tabular data) and distribution shifts, often employing deep learning architectures such as transformers, variational autoencoders, and neural networks (including LSTMs and CNNs). This field is vital for enhancing the reliability and trustworthiness of automated systems, driving advancements in explainability, fairness, and efficiency to ensure responsible deployment in high-stakes domains.

Papers

August 29, 2023

A Comprehensive Augmentation Framework for Anomaly Detection
Jiang Lin, Yaping Yan
Anomaly Detection Data Augmentation Data Augmentation Method Anomaly Detection Model MVTec Anomaly Detection

March 21, 2023

Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images
Yongjin Jeon, Youngtack Oh, Doyoung Jeong, Hyunguk Choi, Junsik Kim
Satellite Image Baseline Result Human Driving Focus Anomaly Detection Model Abnormal Event Detection

November 9, 2022

Deep Learning for Time Series Anomaly Detection: A Survey
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Deep Learning Timely Survey Deep Anomaly Detection Anomaly Detection Model

July 18, 2022

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers
Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh Chawla, Jane Cleland-Huang
Deep Learning Model Financial Application Cyber Physical Anomaly Detection Model Design Specification Deep Learning Based Anomaly Detection Requirement Elicitation

June 16, 2022

Applications of Machine Learning to the Identification of Anomalous ER Claims
Jesse B. Crawford, Nicholas Petela
Machine Learning Financial Application Random Forest Person Identification Anomaly Score Anomaly Detection Model Encoding Model

February 16, 2022

Anomalib: A Deep Learning Library for Anomaly Detection
Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, Utku Genc
Anomaly Detection Unsupervised Anomaly Detection State of the Art Anomaly Anomaly Detection Model Deep Learning Library

February 15, 2022

Trustworthy Anomaly Detection: A Survey
Shuhan Yuan, Xintao Wu
Timely Survey Anomaly Detection Trustworthy Machine Learning Anomaly Detection Model