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 3371–3380 of 4856 papers

Title	Date	Tasks	Status
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data	Dec 5, 2024	Anomaly DetectionSelf-Supervised Learning	—Unverified
Transformation Based Deep Anomaly Detection in Astronomical Images	May 15, 2020	Anomaly DetectionDimensionality Reduction	—Unverified
Transformer-based Multivariate Time Series Anomaly Localization	Jan 15, 2025	Anomaly DetectionAnomaly Localization	—Unverified
Transformer-based normative modelling for anomaly detection of early schizophrenia	Dec 8, 2022	Anomaly Detection	—Unverified
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos	Mar 2, 2025	Anomaly DetectionFew-Shot Learning	—Unverified
TransLog: A Unified Transformer-based Framework for Log Anomaly Detection	Dec 31, 2021	Anomaly Detection	—Unverified
Transparent Anomaly Detection via Concept-based Explanations	Oct 16, 2023	Anomaly DetectionClassification	—Unverified
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem	May 1, 2020	Anomaly DetectionDialogue Evaluation	—Unverified
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components	Mar 21, 2024	Anomaly DetectionVideo Segmentation	—Unverified
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning	Feb 23, 2025	Anomaly DetectionFederated 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	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