Unsupervised Anomaly

Unsupervised anomaly detection focuses on identifying unusual data points without relying on labeled examples, a crucial task in diverse fields like industrial automation and security. Current research emphasizes improving the robustness of reconstruction-based methods, particularly in multi-class settings, and addressing challenges posed by irrelevant features in clustering algorithms. Significant efforts are also dedicated to enhancing the security of anomaly detection models against adversarial attacks and developing efficient, scalable algorithms for high-dimensional data, such as those based on Gaussianization or entropic outlier sparsification. These advancements are vital for improving the reliability and applicability of anomaly detection across various domains.

Papers

November 16, 2024

Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder
Weiming Xu, Peng Zhang
Unsupervised Learning Industrial Anomaly Detection Unsupervised Anomaly LSTM Cell

November 13, 2024

Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors
Stepan Svirin, Artem Ryzhikov, Saraa Ali, Denis Derkach
Machine Learning Artificial Intelligence Algorithm Signature Verification Signature Analysis Unsupervised Anomaly Induction Motor

November 6, 2024

Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging
Yuan Bi, Lucie Huang, Ricarda Clarenbach, Reza Ghotbi, Angelos Karlas, Nassir Navab, Zhongliang Jiang
Unsupervised Anomaly Detection Novel Approach Counterfactual Image Synthetic Anomaly Ultrasound Imaging Unsupervised Anomaly Stage Diffusion

October 29, 2024

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates
Julien Pallage, Bertrand Scherrer, Salma Naccache, Christophe Bélanger, Antoine Lesage-Landry
Anomaly Detection Synthetic Data Trustworthy Machine Learning Open Dataset MLOps System Outlier Event Unsupervised Anomaly

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

February 19, 2024

Cluster Metric Sensitivity to Irrelevant Features
Miles McCrory, Spencer A. Thomas
Data Dimensionality K$ Mean Unsupervised Clustering Unsupervised Anomaly

December 14, 2023

On the Difficulty of Defending Contrastive Learning against Backdoor Attacks
Changjiang Li, Ren Pang, Bochuan Cao, Zhaohan Xi, Jinghui Chen, Shouling Ji, Ting Wang
Contrastive Learning Backdoor Attack Difficulty Level Unsupervised Anomaly

June 23, 2023

Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition
Mireille El-Assal, Pierre Tirilly, Ioan Marius Bilasco
Deep Convolutional Neural Network Action Recognition Spike Timing Dependent Plasticity Convolutional Spiking Neural Network Two Stream Unsupervised Anomaly

June 16, 2023

FABLE : Fabric Anomaly Detection Automation Process
Simon Thomine, Hichem Snoussi, Mahmoud Soua
Anomaly Detection Object Surface Cautionary TALE Unsupervised Anomaly Fabric Defect Texture Anomaly

October 18, 2022

Protecting Split Learning by Potential Energy Loss
Fei Zheng, Chaochao Chen, Lingjuan Lyu, Xinyi Fu, Xing Fu, Weiqiang Wang, Xiaolin Zheng, Jianwei Yin
Split Learning PRIvacy Leakage Privacy Preserving Learning Unsupervised Anomaly Forward Forward

August 8, 2022

Adversarial robustness of VAEs through the lens of local geometry
Asif Khan, Amos Storkey
Latent Space Adversarial Robustness Variational Autoencoder Variational Autoencoders Camera Lens Local Geometry Unsupervised Anomaly

April 12, 2022

Unsupervised Anomaly and Change Detection with Multivariate Gaussianization
José A. Padrón-Hidalgo, Valero Laparra, Gustau Camps-Valls
Anomaly Detection Change Detection Unsupervised Method Gaussian Distribution Multivariate Gaussian Gaussian Data Multivariate Density Unsupervised Anomaly

December 22, 2021

Robust learning of data anomalies with analytically-solvable entropic outlier sparsification
Illia Horenko
Robust Learning Non Gaussian Anomalous Event Unsupervised Anomaly Error Minimizing Noise

November 8, 2021

Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection
Meiling Fang, Fadi Boutros, Arjan Kuijper, Naser Damer
Recognition Task Face Presentation Attack Detection Unsupervised Anomaly

Unsupervised Anomaly

Papers

Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder

Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors

Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging

Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates

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

Cluster Metric Sensitivity to Irrelevant Features

On the Difficulty of Defending Contrastive Learning against Backdoor Attacks

Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition

FABLE : Fabric Anomaly Detection Automation Process

Protecting Split Learning by Potential Energy Loss

Adversarial robustness of VAEs through the lens of local geometry

Unsupervised Anomaly and Change Detection with Multivariate Gaussianization

Robust learning of data anomalies with analytically-solvable entropic outlier sparsification

Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection