Anomaly Detection

1225 papers with code • 66 benchmarks • 95 datasets

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

Benchmarks

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These leaderboards are used to track progress in Anomaly Detection

Dataset	Best Model	Compare
MVTec AD	ReConPatch Ensemble (+RefineNet)	See all
One-class CIFAR-10	CLIP (OE)	See all
MVTec LOCO AD	PUAD	See all
CUHK Avenue	AMSRC	See all
VisA	DiffusionAD	See all
ShanghaiTech	DAC (STG-NF + Jigsaw)	See all
Fishyscapes L&F	cDNP+OE	See all
One-class CIFAR-100	Transformaly	See all
Unlabeled CIFAR-10 vs CIFAR-100	PsudoLabels ViT	See all
Fashion-MNIST	GAN based Anomaly Detection in Imbalance Problems	See all
UBnormal	STG-NF - Supervised	See all
One-class ImageNet-30	BCE-Clip (OE)	See all
Numenta Anomaly Benchmark	our	See all
BTAD	CPR	See all
UCSD Ped2	DMAD	See all
Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix)	PsudoLabels ViT	See all
Fishyscapes	RPL+CoroCL	See all
Road Anomaly	RbA	See all
AeBAD-S	MMR	See all
AeBAD-V	MMR	See all
MNIST	GAN-based Anomaly Detection in Imbalance Problems	See all
Hyper-Kvasir Dataset	CCD	See all
Leave-One-Class-Out CIFAR-10	BCE-CLIP	See all
Leave-One-Class-Out ImageNet-30	BCE-CLIP (OE)	See all
Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200	PsudoLabels CLIP ViT	See all
Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102	PsudoLabels CLIP ViT	See all
LAG	CCD	See all
InsPLAD	AttentDifferNet (SENet-AlexNet)	See all
Cats-and-Dogs	PANDA	See all
DIOR	PANDA-OE	See all
Lost and Found	Mask2Anomaly	See all
MVTEC AD textures	DualModel	See all
Surface Defect Saliency of Magnetic Tile	CS-Flow (unsupervised)	See all
UCSD Peds2	Background-Agnostic Framework	See all
ODDS	kNN	See all
UEA time-series datasets	SINBAD	See all
MVTec AD Textures Domain Generalization	FABLE	See all
voraus-AD	MVT-Flow	See all
Thyroid	DevNet	See all
MVTEC 3D-AD	CDO	See all
Corridor	Two-stream	See all
Vehicle Claims	Random Forest	See all
PAD Dataset	SplatPose	See all
Census	DevNet	See all
MNIST-test	OGNET	See all
CIFAR-10	RCALAD	See all
AG News	DATE	See all
ASSIRA Cat Vs Dog	Shell-based Anomaly (supervisered)	See all
STL-10	Shell-based Anomaly (supervised)	See all
Forest CoverType	DIF	See all
Kaggle-Credit Card Fraud Dataset	DIF	See all
NB15-Analysis	DIF	See all
NB15-Backdoor	DIF	See all
NB15-DoS	DIF	See all
BottleCap	DFC	See all
kdd 99	PCA via oversampling	See all
MVTec 3D-AD (RGB)	CPR	See all
KDD Cup 1999	RCALAD	See all
MIT-BIH Arrhythmia Database	RCALAD	See all
Musk v1	RCALAD	See all
SVHN	RCALAD	See all
KSDD2	SAA+	See all
TII-SSRC-23	Deep SVDD	See all
ADNI	Brainomaly	See all
COCO-OOC	GCRN	See all
Real 3D-AD	Reg 3D-AD	See all

Show all 66 benchmarks

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Libraries

Use these libraries to find Anomaly Detection models and implementations

xuhongzuo/DeepOD

15 papers

284

openvinotoolkit/anomalib

8 papers

3,144

sintel-dev/orion

5 papers

969

yzhao062/pyod

4 papers

7,952

See all 6 libraries.

Datasets

Subtasks

Anomaly Detection In Surveillance Videos

Graph Anomaly Detection

Image Manipulation Detection

Weakly-supervised Anomaly Detection

Abnormal Event Detection In Video

Self-Supervised Anomaly Detection

3D Anomaly Detection

3D Anomaly Detection and Segmentation

RGB+3D Anomaly Detection and Segmentation

Contextual Anomaly Detection

Depth Anomaly Detection and Segmentation

Group Anomaly Detection

RGB+Depth Anomaly Detection and Segmentation

Damaged Tissue Detection

Unsupervised Anomaly Detection In Sound

3D Anomaly Segmentation

Depth Anomaly Segmentation

3D + RGB Anomaly Segmentation

Depth + RGB Anomaly Segmentation

Depth + RGB Anomaly Detection

3D + RGB Anomaly Detection

DepthAnomaly Detection

Most implemented papers

Most implemented Social Latest No code

Deep and Confident Prediction for Time Series at Uber

PawaritL/BayesianLSTM • • 6 Sep 2017

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.

Paper
Code

GluonTS: Probabilistic Time Series Models in Python

awslabs/gluon-ts • • 12 Jun 2019

We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.

Paper
Code

Deep Anomaly Detection with Deviation Networks

GuansongPang/deviation-network • • 19 Nov 2019

Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e. g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail.

Paper
Code

Learning Generalized Spoof Cues for Face Anti-spoofing

vis-var/lgsc-for-fas • • 8 May 2020

In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues.

Paper
Code

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

safe-graph/DGFraud • • 19 Aug 2020

Finally, the selected neighbors across different relations are aggregated together.

Paper
Code

The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain

careerbuilder/semantic-knowledge-graph • 2 Sep 2016

This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph.

Paper
Code

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

ShrishtiHore/Anomaly-Detection-in-CCTV-Surveillance-Videos • • 6 Jan 2017

We present an efficient method for detecting anomalies in videos.

Paper
Code

Deep Sets

lwtnn/lwtnn • NeurIPS 2017

Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong.

Paper
Code