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 151–200 of 506 papers

TitleStatusHype
Anomaly Detection with Adversarially Learned Perturbations of Latent Space—0
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation—0
A Hierarchically Feature Reconstructed Autoencoder for Unsupervised Anomaly Detection—0
Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Coincident Learning for Unsupervised Anomaly Detection—0
Anomaly Detection via Autoencoder Composite Features and NCE—0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations—0
A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection—0
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Challenges for Unsupervised Anomaly Detection in Particle Physics—0
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data—0
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data—0
A general-purpose method for applying Explainable AI for Anomaly Detection—0
Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data—0
F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised—0
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection—0
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
InDeed: Interpretable image deep decomposition with guaranteed generalizability—0
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks—0
Deep Active Learning for Anomaly Detection—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary—0
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms—0
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—0
Bias in Unsupervised Anomaly Detection in Brain MRI—0
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies—0
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
Anomaly detection through latent space restoration using vector-quantized variational autoencoders—0
Explainable Unsupervised Anomaly Detection with Random Forest—0
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection—0
Exploring Dual Model Knowledge Distillation for Anomaly Detection—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
Benchmarking Unsupervised Anomaly Detection and Localization—0
A General Framework for Unsupervised Anomaly Detection—0
Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection—0
Categorical anomaly detection in heterogeneous data using minimum description length clustering—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization—0
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model—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