Anomaly Data

Anomaly detection focuses on identifying data points deviating from established patterns, aiming to improve system safety, security, and efficiency across diverse applications. Current research emphasizes developing robust models that generalize well to unseen anomalies, often employing deep learning architectures like autoencoders and transformers, along with contrastive learning and self-supervised techniques to leverage unlabeled data. This field is crucial for various sectors, from industrial process monitoring and cybersecurity to healthcare and scientific data analysis, enabling proactive identification of critical issues and improved decision-making.

Papers

October 22, 2024

Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning
Kai Zhao, Zhihao Zhuang, Chenjuan Guo, Hao Miao, Yunyao Cheng, Bin Yang
Anomaly Data Unsupervised Time Series Anomaly Detection Unveiling Hidden Energy Anomaly False in Season Anomaly Suppression

September 21, 2024

Local Patterns Generalize Better for Novel Anomalies
Yalong Jiang, Liquan Mao
Video Anomaly Detection Video Sequence Anomaly Data Local Binary Pattern

August 9, 2024

Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity Levels
Wonjun Yi, Yong-Hwa Park, Wonho Jung
Anomaly Score Performance Metric ROC Curve Anomaly Detector Severity Classification Anomaly Data

May 29, 2024

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka
Training Data Anomaly Score Positive Unlabeled Learning Semi Supervised Anomaly Detection Anomaly Detector Anomaly Data

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 Unsupervised Anomaly Refinement Transformer Anomaly Discrimination Multi Class Out of Distribution

April 12, 2024

HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies
Haili Sun, Yan Huang, Lansheng Han, Cai Fu, Chunjie Zhou
Anomaly Detection Accurate Detection Anomaly Data Anomaly Detection Capability Industrial Time Series

March 13, 2024

Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows
Zhangxuan Dang, Yu Zheng, Xinglin Lin, Chunlei Peng, Qiuyu Chen, Xinbo Gao
Anomaly Detection Semi Supervised Learning Normalizing Flow Anomaly Sample Anomaly Data Anomaly Detection Framework Network Traffic Anomaly Detection

February 19, 2024

SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning
Jongha Lee, Sunwoo Kim, Kijung Shin
Anomaly Detection Self Supervised Learning Label Information Real World Graph Anomaly Data

August 25, 2023

Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs
Tom Westermann, Milapji Singh Gill, Alexander Fay
Knowledge Graph Cyber Physical Anomaly Data Alternating Time Temporal Logic Automaton Model

July 24, 2023

Few-shot $\mathbf{1/a}$ Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance
Takato Yasuno
Class Imbalance Shot Scenario Online Damage Anomaly Data Spatial Bias

July 16, 2023

LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection
Haonan Yin, Guanlong Jiao, Qianhui Wu, Borje F. Karlsson, Biqing Huang, Chin Yew Lin
Latent Diffusion Model Unsupervised Anomaly Detection Feature Engineering Anomaly Data Multi Class Unsupervised Anomaly Detection

April 15, 2023

Few-shot Weakly-supervised Cybersecurity Anomaly Detection
Rahul Kale, Vrizlynn L. L. Thing
Anomaly Detection Shot Anomaly Detection Cybersecurity Domain Anomaly Data Anomaly Detection Framework

March 24, 2023

Anomaly Detection under Distribution Shift
Tri Cao, Jiawen Zhu, Guansong Pang
Anomaly Detection Distribution Shift Anomaly Data Robust Anomaly Detection Unified Generalization

February 10, 2023

Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection
Hang Zhou, Junqing Yu, Wei Yang
Convolutional Neural Network Latent Space Discriminative Feature Anomaly Data Weakly Supervised Video Anomaly Detection Memory System

January 11, 2023

Anomalies, Representations, and Self-Supervision
Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn
Contrastive Learning Anomaly Detection Meaningful Representation Self Supervision Unlabeled Anomaly Anomaly Data

December 31, 2022

Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment
Eleonora Achiluzzi, Menglu Li, Md Fahd Al Georgy, Rasha Kashef
Anomaly Detection Internet of Thing Data Driven Approach Good Thing Based Anomaly Detection State of the Art Anomaly Anomaly Detection Algorithm Anomaly Data

December 21, 2022

Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
Anomaly Detection Time Series Unsupervised Anomaly Detection Detecting Anomaly Current Method Anomaly Data Anomaly Type

November 28, 2022

A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI
Ayantika Das, Arun Palla, Keerthi Ram, Mohanasankar Sivaprakasam
Anomaly Detection Study Feature Unsupervised Anomaly Detection Unsupervised Method Brain MRI Prime Factorization Anomaly Data

November 16, 2022

Are we certain it's anomalous?
Alessandro Flaborea, Bardh Prenkaj, Bharti Munjal, Marco Aurelio Sterpa, Dario Aragona, Luca Podo, Fabio Galasso
Anomaly Detection Anomaly Data False in Season Anomaly Suppression

September 26, 2022

Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine Learning
Sajjad Asefi, Mile Mitrovic, Dragan Ćetenović, Victor Levi, Elena Gryazina, Vladimir Terzija
Machine Learning State Estimation Power System Anomaly Data System State Estimation

Anomaly Data

Papers

Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning

Local Patterns Generalize Better for Novel Anomalies

Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity Levels

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

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

HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies

Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows

SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning

Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs

Few-shot $\mathbf{1/a}$ Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance

LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection

Few-shot Weakly-supervised Cybersecurity Anomaly Detection

Anomaly Detection under Distribution Shift

Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection

Anomalies, Representations, and Self-Supervision

Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment

Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series

A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI

Are we certain it's anomalous?

Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine Learning