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 3626–3650 of 4856 papers

TitleStatusHype
ANOMALY DETECTION WITH FRAME-GROUP ATTENTION IN SURVEILLANCE VIDEOS—0
Anomaly Detection with Generative Adversarial Networks—0
Anomaly Detection with HMM Gauge Likelihood Analysis—0
Anomaly Detection with Inexact Labels—0
Anomaly Detection with Joint Representation Learning of Content and Connection—0
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings—0
Anomaly Detection with Score functions based on Nearest Neighbor Graphs—0
Anomaly Detection with SDAE—0
Anomaly detection with semi-supervised classification based on risk estimators—0
Anomaly Detection with Tensor Networks—0
Anomaly Detection with Test Time Augmentation and Consistency Evaluation—0
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm—0
Anomaly detection with Wasserstein GAN—0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
Anomaly Locality in Video Surveillance—0
Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon—0
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks—0
Anomaly Rule Detection in Sequence Data—0
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells—0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
Anomize: Better Open Vocabulary Video Anomaly Detection—0
AnomMAN: Detect Anomaly on Multi-view Attributed Networks—0
An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices—0
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces—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