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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3581–3590 of 4856 papers

Title	Date	Tasks	Status
Weakly Supervised Detection of Baby Cry	Apr 19, 2023	Anomaly DetectionSupervised Anomaly Detection	—Unverified
Weakly-supervised Joint Anomaly Detection and Classification	Aug 20, 2021	Anomaly DetectionClassification	—Unverified
Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder	Jul 13, 2023	Anomaly DetectionAnomaly Localization	—Unverified
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video	Aug 9, 2021	Anomaly Detection	—Unverified
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance	Dec 16, 2022	Anomaly DetectionClustering	—Unverified
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts	Aug 12, 2024	Anomaly DetectionEvent Detection	—Unverified
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection	Feb 1, 2022	Anomaly DetectionTime Series Analysis	—Unverified
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks	Aug 8, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified
What is AI, what is it not, how we use it in physics and how it impacts... you	Apr 2, 2025	Anomaly DetectionMisconceptions	—Unverified
What makes a good data augmentation for few-shot unsupervised image anomaly detection?	Apr 6, 2023	Anomaly DetectionData Augmentation	—Unverified

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified