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 351–400 of 506 papers

TitleStatusHype
Statistically Significant kNNAD by Selective Inference—0
Steam Turbine Anomaly Detection: An Unsupervised Learning Approach Using Enhanced Long Short-Term Memory Variational Autoencoder—0
STemGAN: spatio-temporal generative adversarial network for video anomaly detection—0
Strengthening Anomaly Awareness—0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators—0
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models—0
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation—0
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development—0
Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms—0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
Toward Supervised Anomaly Detection—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
UaiNets: From Unsupervised to Active Deep Anomaly Detection—0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance—0
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving—0
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis—0
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks—0
Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder—0
Unsupervised anomaly detection for discrete sequence healthcare data—0
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation—0
Unsupervised Anomaly Detection From Semantic Similarity Scores—0
Unsupervised Anomaly Detection from Time-of-Flight Depth Images—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction—0
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data—0
Unsupervised anomaly detection in digital pathology using GANs—0
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention—0
Unsupervised anomaly detection in MeV ultrafast electron diffraction—0
Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information—0
Unsupervised Anomaly Detection in Parole Hearings using Language Models—0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks—0
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods—0
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus—0
Unsupervised anomaly detection on cybersecurity data streams: a case with BETH dataset—0
Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection—0
Unsupervised Anomaly Detection on Temporal Multiway Data—0
Unsupervised Anomaly Detection Using Diffusion Trend Analysis—0
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling—0
Unsupervised Anomaly Detection via Nonlinear Manifold Learning—0
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Unsupervised Brain Anomaly Detection and Segmentation with Transformers—0
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models—0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals—0
Unsupervised Deep Learning for IoT Time Series—0
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks—0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks—0
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data—0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection—0
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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