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 4071–4080 of 4856 papers

Title	Date	Tasks	Status
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond	Apr 26, 2024	Anomaly DetectionCell Detection	CodeCode Available
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD	Nov 23, 2022	Anomaly DetectionOutlier Detection	CodeCode Available
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring	Jun 15, 2022	Anomaly DetectionDimensionality Reduction	CodeCode Available
DMAD: Dual Memory Bank for Real-World Anomaly Detection	Mar 19, 2024	Anomaly DetectionRepresentation Learning	CodeCode Available
STAN: Synthetic Network Traffic Generation with Generative Neural Models	Sep 27, 2020	Anomaly Detection	CodeCode Available
Local2Global: A distributed approach for scaling representation learning on graphs	Jan 12, 2022	Anomaly DetectionGraph Representation Learning	CodeCode Available
Are generative deep models for novelty detection truly better?	Jul 13, 2018	Anomaly DetectionNovelty Detection	CodeCode Available
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection	Jul 8, 2019	Anomaly Detection	CodeCode Available
Learning Temporal Regularity in Video Sequences	Apr 15, 2016	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available
Statistical Anomaly Detection via Composite Hypothesis Testing for Markov Models	Feb 27, 2017	Anomaly DetectionTwo-sample testing	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