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

Showing 176–200 of 4856 papers

TitleStatusHype
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly DetectionCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Component-aware anomaly detection framework for adjustable and logical industrial visual inspectionCode1
Collaborative Discrepancy Optimization for Reliable Image Anomaly LocalizationCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New BaselineCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous DrivingCode1
Active Anomaly Detection via EnsemblesCode1
Class Label-aware Graph Anomaly DetectionCode1
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth SimulationCode1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
Classification-Based Anomaly Detection for General DataCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection TechniquesCode1
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRICode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
CHAD: Charlotte Anomaly DatasetCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
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Benchmark Results

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