Anomaly Detection
1233 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
These leaderboards are used to track progress in Anomaly Detection
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Libraries
Use these libraries to find Anomaly Detection models and implementationsDatasets
CIFAR-10
CIFAR-100
MNIST
SVHN
Fashion-MNIST
STL-10
AG News
MVTecAD
ShanghaiTech
ShanghaiTech Campus
Subtasks
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Unsupervised Anomaly Detection
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One-Class Classification
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Supervised Anomaly Detection
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Anomaly Detection In Surveillance Videos
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Anomaly Detection In Surveillance Videos
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Graph Anomaly Detection
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Image Manipulation Detection
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Weakly-supervised Anomaly Detection
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Abnormal Event Detection In Video
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Self-Supervised Anomaly Detection
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3D Anomaly Detection
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3D Anomaly Detection and Segmentation
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RGB+3D Anomaly Detection and Segmentation
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Contextual Anomaly Detection
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Depth Anomaly Detection and Segmentation
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Group Anomaly Detection
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RGB+Depth Anomaly Detection and Segmentation
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Damaged Tissue Detection
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Unsupervised Anomaly Detection In Sound
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3D Anomaly Segmentation
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Depth Anomaly Segmentation
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3D + RGB Anomaly Segmentation
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Depth + RGB Anomaly Segmentation
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Depth + RGB Anomaly Detection
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3D + RGB Anomaly Detection
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DepthAnomaly Detection
Latest papers
FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discrimination
Nowadays large computers extensively output logs to record the runtime status and it has become crucial to identify any suspicious or malicious activities from the information provided by the realtime logs.
SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection
Detecting anomalies in images has become a well-explored problem in both academia and industry.
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
zhangzjn/ader
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Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering.
Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection
We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy.
MedIAnomaly: A comparative study of anomaly detection in medical images
Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns.
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection
We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective, subsequently solved through K-SVD-type iterative algorithms.
Foundation Models for Structural Health Monitoring
For AD, we achieve a near-perfect 99. 9% accuracy with a monitoring time span of just 15 windows.
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline
anasemad11/clap
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Unsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity recently due to its practical real-world applications.
On the True Distribution Approximation of Minimum Bayes-Risk Decoding
cyberagentailab/mbr-anomaly
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Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation.
