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 3241–3250 of 4856 papers

Title	Date	Tasks	Status
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos	Feb 14, 2022	Anomaly DetectionGraph Learning	—Unverified
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection	Feb 13, 2022	Anomaly DetectionSuperpixels	—Unverified
An Automated Analysis Framework for Trajectory Datasets	Feb 12, 2022	Anomaly Detection	—Unverified
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data	Feb 11, 2022	Anomaly DetectionChange Point Detection	—Unverified
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach	Feb 11, 2022	Anomaly DetectionContrastive Learning	—Unverified
Detecting out-of-context objects using contextual cues	Feb 11, 2022	Anomaly DetectionObject	—Unverified
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system	Feb 11, 2022	Anomaly DetectionMeta-Learning	—Unverified
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data	Feb 10, 2022	Anomaly DetectionTime Series	—Unverified
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection	Feb 9, 2022	Anomaly DetectionFederated Learning	—Unverified
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion	Feb 8, 2022	Anomaly DetectionContrastive Learning	—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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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