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 101150 of 4856 papers

TitleStatusHype
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2Code2
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly DetectionCode2
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting MaskCode2
Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video AnomalyCode2
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality LocalizationCode2
MedIAnomaly: A comparative study of anomaly detection in medical imagesCode2
SoftPatch: Unsupervised Anomaly Detection with Noisy DataCode2
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly DetectionCode2
PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global FeaturesCode2
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online AdaptationCode2
Generative Semi-supervised Graph Anomaly DetectionCode2
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection ModelsCode2
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency PerspectiveCode2
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly DetectionCode2
LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly DetectionCode2
Unsupervised Continual Anomaly Detection with Contrastively-learned PromptCode2
Uncovering What Why and How: A Comprehensive Benchmark for Causation Understanding of Video AnomalyCode2
GenDet: Towards Good Generalizations for AI-Generated Image DetectionCode2
DiAD: A Diffusion-based Framework for Multi-class Anomaly DetectionCode2
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly DetectionCode2
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly DetectionCode2
Solving Data Quality Problems with Desbordante: a DemoCode2
Generative AI for Medical Imaging: extending the MONAI FrameworkCode2
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly DetectionCode2
FITS: Modeling Time Series with 10k ParametersCode2
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly DetectionCode2
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and ProspectsCode2
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly DetectionCode2
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time SeriesCode2
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
Segment Any Anomaly without Training via Hybrid Prompt RegularizationCode2
SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationCode2
WinCLIP: Zero-/Few-Shot Anomaly Classification and SegmentationCode2
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesCode2
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly DetectionCode2
Multimodal Industrial Anomaly Detection via Hybrid FusionCode2
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
Streaming Anomaly DetectionCode2
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and SegmentationCode2
Registration based Few-Shot Anomaly DetectionCode2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
A Unified Model for Multi-class Anomaly DetectionCode2
Rethinking Graph Neural Networks for Anomaly DetectionCode2
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalitiesCode2
Log-based Anomaly Detection with Deep Learning: How Far Are We?Code2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series DataCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified