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 4201–4210 of 4856 papers

Title	Date	Tasks	Status
Future Frame Prediction Using Convolutional VRNN for Anomaly Detection	Sep 5, 2019	Anomaly DetectionPrediction	—Unverified
Subset Multivariate Collective And Point Anomaly Detection	Sep 4, 2019	Anomaly Detection	—Unverified
Filtering Approaches for Dealing with Noise in Anomaly Detection	Sep 3, 2019	Anomaly Detection	—Unverified
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Network	Sep 2, 2019	Anomaly DetectionTime Series	CodeCode Available
Deep Actor-Critic Reinforcement Learning for Anomaly Detection	Aug 28, 2019	Anomaly DetectionDeep Reinforcement Learning	—Unverified
Detection of Backdoors in Trained Classifiers Without Access to the Training Set	Aug 27, 2019	Anomaly DetectionData Poisoning	—Unverified
Self-Supervised Representation Learning via Neighborhood-Relational Encoding	Aug 27, 2019	Anomaly DetectionDecoder	—Unverified
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach	Aug 25, 2019	Anomaly DetectionDeep Learning	—Unverified
A framework for anomaly detection using language modeling, and its applications to finance	Aug 24, 2019	Anomaly DetectionLanguage Modeling	—Unverified
Heterogeneous Relational Kernel Learning	Aug 24, 2019	Anomaly DetectionClustering	—Unverified

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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