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 3076–3100 of 4856 papers

TitleStatusHype
Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments—0
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning—0
WEAC: Word embeddings for anomaly classification from event logs—0
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection—0
Weakly-supervised anomaly detection for multimodal data distributions—0
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network—0
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment—0
Weakly Supervised Detection of Baby Cry—0
Weakly-supervised Joint Anomaly Detection and Classification—0
Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder—0
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video—0
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance—0
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts—0
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection—0
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks—0
What is AI, what is it not, how we use it in physics and how it impacts... you—0
What makes a good data augmentation for few-shot unsupervised image anomaly detection?—0
What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23—0
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time—0
When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection—0
When to Intervene: Detecting Abnormal Mood using Everyday Smartphone Conversations—0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
Which principal components are most sensitive to distributional changes?—0
White Functionals for Anomaly Detection in Dynamical Systems—0
Why is the Mahalanobis Distance Effective for Anomaly Detection?—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