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 511–520 of 4856 papers

Title	Date	Tasks	Status	Hype
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing	Jan 31, 2023	Anomaly DetectionContinual Learning	CodeCode Available	1
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays	Jan 30, 2023	Anomaly DetectionRepresentation Learning	CodeCode Available	1
Making Reconstruction-based Method Great Again for Video Anomaly Detection	Jan 28, 2023	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1
Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly Detection	Jan 27, 2023	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	1
Quantum anomaly detection in the latent space of proton collision events at the LHC	Jan 25, 2023	Anomaly DetectionQuantum Machine Learning	CodeCode Available	1
The role of noise in denoising models for anomaly detection in medical images	Jan 19, 2023	Anomaly DetectionDenoising	CodeCode Available	1
Hybrid Open-set Segmentation with Synthetic Negative Data	Jan 19, 2023	Anomaly DetectionSegmentation	CodeCode Available	1
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection	Jan 17, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks	Jan 17, 2023	Anomaly Detection	CodeCode Available	1
On Advantages of Mask-level Recognition for Outlier-aware Segmentation	Jan 9, 2023	Anomaly DetectionSemantic Segmentation	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