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 361–370 of 4856 papers

Title	Date	Tasks	Status	Hype
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection	Nov 5, 2023	Anomaly DetectionQuestion Answering	CodeCode Available	1
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead	Nov 5, 2023	3D Anomaly DetectionAnomaly Detection	CodeCode Available	1
Holistic Representation Learning for Multitask Trajectory Anomaly Detection	Nov 3, 2023	Anomaly DetectionDecoder	CodeCode Available	1
InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images	Nov 2, 2023	Anomaly DetectionImage Classification	CodeCode Available	1
Time Series Anomaly Detection using Diffusion-based Models	Nov 2, 2023	Anomaly DetectionTime Series	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
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection	Oct 29, 2023	Anomaly DetectionImage Captioning	CodeCode Available	1
Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores	Oct 27, 2023	Anomaly Detection	CodeCode Available	1
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection	Oct 26, 2023	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Towards Self-Interpretable Graph-Level Anomaly Detection	Oct 25, 2023	Anomaly DetectionGraph Anomaly Detection	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	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