Anomaly Discrimination

Anomaly discrimination focuses on identifying unusual patterns or events within datasets, a crucial task across diverse fields. Current research emphasizes developing robust methods that address challenges like limited data, imbalanced classes, and the need for explainable results, exploring techniques such as dissimilarity-based approaches, transformer networks, and rule-based reasoning frameworks integrated with large language models. These advancements improve anomaly detection accuracy and interpretability in applications ranging from video surveillance and time series analysis to medical imaging and blockchain security, ultimately enhancing the reliability and trustworthiness of automated systems.

Papers

July 14, 2024

Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
Yuchen Yang, Kwonjoon Lee, Behzad Dariush, Yinzhi Cao, Shao-Yuan Lo
Complex Reasoning Video Anomaly Detection Simple RULE Shot Prompting Robust Reasoning Anomaly Discrimination

May 18, 2024

SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection
Zhijie Zhong, Zhiwen Yu, Xing Xi, Yue Xu, Jiahui Chen, Kaixiang Yang
Anomalous Data Anomaly Discrimination

April 30, 2024

Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton
Jerry Chun-Wei Lin, Pi-Wei Chen, Chao-Chun Chen
Anomaly Detection Reconstruction Network Anomaly Data Linear Feature Refinement Transformer Unsupervised Anomaly Anomaly Discrimination Multi Class Out of Distribution

January 7, 2024

Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis
Mohammad Hasan, Mohammad Shahriar Rahman, Helge Janicke, Iqbal H. Sarker
Anomaly Detection Detecting Anomaly Machine Learning Classifier Explanation Model Blockchain Transaction Anomaly Discrimination

January 6, 2024

Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
Seyed Mohammad Hossein Hashemi, Leila Safari, Amirhossein Dadashzadeh Taromi
Deep Learning Vision Transformer Medical Image Medical Imaging Brain Tumor Look Once Version 8 Tumor Sub Region Anomaly Discrimination

August 30, 2023

Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality
Peizheng Li, Adnan Aijaz, Tim Farnham, Sajida Gufran, Sita Chintalapati
Digital Twin Massive Connectivity G Wireless Anomaly Detection Capability Innovative DT Design Anomaly Discrimination

April 11, 2023

Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
YanMing Hu, Chuan Chen, BoWen Deng, YuJing Lai, Hao Lin, ZiBin Zheng, Jing Bian
Anomaly Detection Self Supervised Learning Representation Learning Graph Drawing Anomaly Discrimination

Anomaly Discrimination

Papers

Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models

SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection

Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton

Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis

Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT

Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality

Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph