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 151–175 of 4856 papers

TitleStatusHype
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
A Unified Model for Multi-class Anomaly DetectionCode2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost FilteringCode2
Is Space-Time Attention All You Need for Video Understanding?Code2
Learning to Detect Multi-class Anomalies with Just One Normal Image PromptCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
LogLLM: Log-based Anomaly Detection Using Large Language ModelsCode2
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality LocalizationCode2
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly DetectionCode2
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
Classification-Based Anomaly Detection for General DataCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly DetectionCode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRICode1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
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