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 2221–2230 of 4856 papers

Title	Date	Tasks	Status
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection	Dec 4, 2023	Anomaly Detectionbackdoor defense	CodeCode Available
Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations	Dec 4, 2023	Anomaly DetectionDomain Adaptation	—Unverified
From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety	Dec 4, 2023	Activity RecognitionAnomaly Detection	—Unverified
ADT: Agent-based Dynamic Thresholding for Anomaly Detection	Dec 3, 2023	Anomaly Detection	—Unverified
Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection	Dec 3, 2023	Anomaly DetectionBinary Classification	CodeCode Available
Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach	Dec 3, 2023	Anomaly Detection	—Unverified
Bagged Regularized k-Distances for Anomaly Detection	Dec 2, 2023	Anomaly DetectionDensity Estimation	—Unverified
Online Anomaly Detection over Live Social Video Streaming	Dec 1, 2023	Anomaly DetectionDecoder	—Unverified
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection	Nov 30, 2023	Anomaly DetectionSelf-Supervised Learning	—Unverified
Unsupervised textile defect detection using convolutional neural networks	Nov 30, 2023	Anomaly DetectionDefect Detection	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	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