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 101–150 of 506 papers

TitleStatusHype
Anomaly Detection using Score-based Perturbation ResilienceCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly DetectionCode1
Unsupervised Anomaly Detection for X-Ray ImagesCode1
Iterative energy-based projection on a normal data manifold for anomaly localizationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Fully Convolutional Cross-Scale-Flows for Image-based Defect DetectionCode1
Unsupervised Anomaly Localization with Structural Feature-AutoencodersCode1
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
Federated PCA on Grassmann Manifold for IoT Anomaly DetectionCode1
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training DataCode1
FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language ModelCode1
MemStream: Memory-Based Streaming Anomaly DetectionCode1
Diffusion Models with Implicit Guidance for Medical Anomaly DetectionCode1
DSR -- A dual subspace re-projection network for surface anomaly detectionCode1
Correlation-aware Deep Generative Model for Unsupervised Anomaly DetectionCode1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing FlowsCode1
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device FailureCode1
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature ImportanceCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesCode1
AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-DecoderCode1
Label-Free Multivariate Time Series Anomaly DetectionCode1
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and LocalizationCode1
Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational AutoencoderCode1
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection AlgorithmsCode1
Deep Anomaly Detection on Tennessee Eastman Process Data—0
Deep Anomaly Detection in Text—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
An optimization method for out-of-distribution anomaly detection models—0
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection—0
A2Log: Attentive Augmented Log Anomaly Detection—0
AI for human assessment: What do professional assessors need?—0
Anomaly Detection with Inexact Labels—0
Fairness-aware Anomaly Detection via Fair Projection—0
ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection—0
Constructing a meta-learner for unsupervised anomaly detection—0
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers—0
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies—0
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!—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