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 3531–3540 of 4856 papers

Title	Date	Tasks	Status
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories	Apr 19, 2024	Anomaly DetectionTrajectory Prediction	—Unverified
VALD-GAN: video anomaly detection using latent discriminator augmented GAN	Oct 18, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Variational autoencoder-based neural network model compression	Aug 25, 2024	Anomaly DetectionImage Generation	—Unverified
Variational Autoencoders for Anomaly Detection in Respiratory Sounds	Aug 5, 2022	Anomaly Detection	—Unverified
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs	Jan 20, 2021	Anomaly DetectionSSIM	—Unverified
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series	Feb 23, 2016	Anomaly DetectionTime Series	—Unverified
Variation and generality in encoding of syntactic anomaly information in sentence embeddings	Nov 12, 2021	Anomaly DetectionSentence	—Unverified
VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models	Dec 2, 2024	Anomaly DetectionVideo Anomaly Detection	—Unverified
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders	Sep 25, 2019	Anomaly DetectionOutlier Detection	—Unverified
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT	Oct 14, 2022	Anomaly DetectionIntrusion Detection	—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