SOTAVerified

Unsupervised Anomaly Detection

The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%. Since anomalies are rare and unknown to the user at training time, anomaly detection in most cases boils down to the problem of modelling the normal data distribution and defining a measurement in this space in order to classify samples as anomalous or normal. In high-dimensional data such as images, distances in the original space quickly lose descriptive power (curse of dimensionality) and a mapping to some more suitable space is required.

Source: Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training

Papers

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Showing 201–250 of 506 papers

TitleStatusHype
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Learning Normal Patterns in Musical Loops—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—0
Forensic Data Analytics for Anomaly Detection in Evolving Networks—0
Bias in Unsupervised Anomaly Detection in Brain MRI—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets—0
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics—0
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis—0
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation—0
Benchmarking Unsupervised Anomaly Detection and Localization—0
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
A General Framework for Unsupervised Anomaly Detection—0
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
Bagged Regularized k-Distances for Anomaly Detection—0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data—0
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching—0
A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection—0
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI—0
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection—0
Label-Free Segmentation of COVID-19 Lesions in Lung CT—0
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection—0
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All datasetsAnoShiftSMAPVehicle ClaimsFashion-MNISTKolektorSDD220NEWSAeBAD-SCaltech-101DAGM2007ECG5000KolektorSDDMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACR-NTL (zero-shot, test anomaly ratio=1%)ROC-AUC FAR62.5—Unverified
2ACR-DSVDD (zero-shot, anomaly ratio=1%)ROC-AUC FAR62—Unverified
3ACR-NTL (zero-shot, test anomaly ratio=20%)ROC-AUC FAR62—Unverified
4ACR-DSVDD (zero-shot, anomaly ratio=20%)ROC-AUC FAR59.1—Unverified
5COPODROC-AUC FAR50.42—Unverified
6OC-SVMROC-AUC FAR49.57—Unverified
7SO-GAALROC-AUC FAR49.35—Unverified
8ECOD Li et al. (2022)ROC-AUC FAR49.19—Unverified
9LOFROC-AUC FAR34.96—Unverified
10deepSVDDROC-AUC FAR34.53—Unverified
#ModelMetricClaimedVerifiedStatus
1DFM (flow matching)F194.1—Unverified
2ContextFlow++ (Glow-based)F193.62—Unverified
3TranAdF189.15—Unverified
4MTAD-GATF188.8—Unverified
5CAE-MF188.27—Unverified
6OmniAnomalyF187.28—Unverified
7GlowF186.05—Unverified
8GDNF185.18—Unverified
9USADF181.86—Unverified
#ModelMetricClaimedVerifiedStatus
1SOMAUC65.43—Unverified
2Isolation ForestAUC59.42—Unverified
3Latent Outlier ExposureAUC58.59—Unverified
4NeuTraL-ADAUC57.03—Unverified
5RSRAEAUC55.38—Unverified
6SOM-DAGMMAUC53.82—Unverified
7Local Outlier FactorAUC52.86—Unverified
8One Class Support Vector MachinesAUC51.68—Unverified
9DAGMMAUC51.22—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC-ROC0.85—Unverified
2RSRAEAUC (outlier ratio = 0.5)0.83—Unverified
3RSRAEAUC-ROC0.75—Unverified
4RSRAEAUC-ROC0.69—Unverified
5RSRAEAUC-ROC0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-orthogonalSegmentation AUROC98.1—Unverified
2WeakREST-UnSegmentation AP76.9—Unverified
3DSRSegmentation AP61.4—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1MSFRDetection AUROC87.1—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffusionADDetection AUROC99.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAUC0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-orthogonalSegmentation AUROC96—Unverified
#ModelMetricClaimedVerifiedStatus
1LVADAUROC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1DyEdgeGATAUC0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1RSRAEAUC (outlier ratio = 0.5)0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1TranADPrecision92.62—Unverified
#ModelMetricClaimedVerifiedStatus
1LVADAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1DyEdgeGATAUC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1P-CAE W-MSE (Tilted View)AUROC78.1—Unverified