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 561–570 of 4856 papers

Title	Date	Tasks	Status	Hype
CHAD: Charlotte Anomaly Dataset	Dec 19, 2022	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Challenges in Visual Anomaly Detection for Mobile Robots	Sep 22, 2022	Anomaly Detection	CodeCode Available	1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown	Jul 10, 2023	Anomaly DetectionCode Generation	CodeCode Available	1
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation	Nov 2, 2023	Anomaly DetectionDepth Anomaly Detection and Segmentation	CodeCode Available	1
Class Label-aware Graph Anomaly Detection	Aug 22, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection	Dec 9, 2022	Anomaly DetectionMultiple Instance Learning	CodeCode Available	1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification	Dec 9, 2020	Anomaly DetectionAtrial Fibrillation Detection	CodeCode Available	1
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization	Feb 17, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1
AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn	Jun 3, 2023	Anomaly DetectionDeep Learning	CodeCode Available	1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning	Dec 6, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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