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 4371–4380 of 4856 papers

Title	Date	Tasks	Status
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark	May 4, 2020	Anomaly Detection	CodeCode Available
Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders	Dec 16, 2024	Anomaly Detection	CodeCode Available
An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw Cycle	Jul 4, 2024	Anomaly DetectionRetrieval	CodeCode Available
Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding	Jul 3, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series	Dec 5, 2018	Anomaly DetectionTime Series	CodeCode Available
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection	Mar 30, 2019	Anomaly DetectionDecoder	CodeCode Available
Temporal anomaly detection: calibrating the surprise	May 29, 2017	Anomaly Detection	CodeCode Available
Anomaly Detection with Density Estimation	Jan 14, 2020	Anomaly DetectionDensity Estimation	CodeCode Available
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection	Nov 4, 2024	Anomaly DetectionAttribute	CodeCode Available
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering	Jun 10, 2019	Anomaly DetectionSparse Learning	CodeCode Available

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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