SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

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Showing 3601–3650 of 4856 papers

TitleStatusHype
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions—0
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
Towards Fair Deep Anomaly Detection—0
Detecting Anomalous Invoice Line Items in the Legal Case Lifecycle—0
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On AutoencoderCode0
Time-Window Group-Correlation Support vs. Individual Features: A Detection of Abnormal UsersCode0
Neural Networks, Artificial Intelligence and the Computational Brain—0
Camouflaged Object Detection and Tracking: A Survey—0
Graph Convolutional Networks for traffic anomalyCode1
Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies—0
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge GraphsCode1
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based ApproachCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Dual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection—0
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation—0
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation—0
Image-Based Jet Analysis—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative FrameworkCode0
A Systematic Mapping Study in AIOps—0
Multi-Modal Anomaly Detection for Unstructured and Uncertain EnvironmentsCode1
GAN Ensemble for Anomaly DetectionCode1
DFR: Deep Feature Reconstruction for Unsupervised Anomaly SegmentationCode1
Anomaly detection through latent space restoration using vector-quantized variational autoencoders—0
Computer Vision and Normalizing Flow-Based Defect DetectionCode0
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning—0
Acoustic Leak Detection in Water Networks—0
Intrinsic persistent homology via density-based metric learningCode1
Comparison of Anomaly Detectors: Context MattersCode0
Water Level Estimation Using Sentinel-1 Synthetic Aperture Radar Imagery And Digital Elevation Models—0
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly DetectionCode1
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework—0
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems—0
Modeling asset allocation strategies and a new portfolio performance score—0
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads—0
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
ESAD: End-to-end Deep Semi-supervised Anomaly Detection—0
Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach—0
Efficient Nonlinear RX Anomaly Detectors—0
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications—0
Perfect density models cannot guarantee anomaly detection—0
No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems—0
Data-Efficient Methods for Dialogue Systems—0
Deep Learning for Medical Anomaly Detection -- A Survey—0
Video Anomaly Detection by Estimating Likelihood of RepresentationsCode0
Unsupervised Anomaly Detection From Semantic Similarity Scores—0
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network—0
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified