Anomaly Detection
1259 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 with no code
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2
The reduced overhead, coupled with its outstanding few-shot performance, makes AnomalyDINO a strong candidate for fast deployment, for example, in industrial contexts.
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images, serving as an alternative to the conventional one-class-one-model setup.
Large language models can be zero-shot anomaly detectors for time series?
First, we present a prompt-based detection method that directly asks a language model to indicate which elements of the input are anomalies.
Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes
In a context of a continuous digitalisation of processes, organisations must deal with the challenge of detecting anomalies that can reveal suspicious activities upon an increasing volume of data.
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
In addition, learning an anomaly detector with limited (or no) anomalies often yields poor prediction performance.
GNN-based Anomaly Detection for Encoded Network Traffic
The early research report explores the possibility of using Graph Neural Networks (GNNs) for anomaly detection in internet traffic data enriched with information.
Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD
Recent studies of multi-modal Industrial Anomaly Detection (IAD) based on point clouds and RGB images indicated the importance of exploiting redundancy and complementarity among modalities for accurate classification and segmentation.
Multimodal video analysis for crowd anomaly detection using open access tourism cameras
In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series from video format using a multimodal approach.
Automated Anomaly Detection on European XFEL Klystrons
High-power multi-beam klystrons represent a key component to amplify RF to generate the accelerating field of the superconducting radio frequency (SRF) cavities at European XFEL.
ECATS: Explainable-by-design concept-based anomaly detection for time series
Deep learning methods for time series have already reached excellent performances in both prediction and classification tasks, including anomaly detection.
