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 4341–4350 of 4856 papers

Title	Date	Tasks	Status
High-dimensional and Permutation Invariant Anomaly Detection	Jun 6, 2023	Anomaly DetectionDensity Estimation	CodeCode Available
Adversarially Learned One-Class Classifier for Novelty Detection	Feb 25, 2018	Anomaly DetectionNovelty Detection	CodeCode Available
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection	Jul 24, 2022	Anomaly DetectionContrastive Learning	CodeCode Available
Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles	Sep 6, 2020	Anomaly DetectionOutlier Detection	CodeCode Available
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data	Feb 17, 2019	Anomaly DetectionClustering	CodeCode Available
Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination	Nov 14, 2024	Anomaly Detection	CodeCode Available
Robust, Deep and Inductive Anomaly Detection	Apr 22, 2017	Anomaly Detection	CodeCode Available
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection	Apr 2, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability	Apr 11, 2024	Anomaly DetectionLanguage Modelling	CodeCode Available
Hashing for Structure-based Anomaly Detection	May 16, 2025	Anomaly Detection	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