Anomaly Detection
1228 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
IPAD: Industrial Process Anomaly Detection Dataset
Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes.
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories.
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing
CUMAD can effectively reduce the number of false alerts in detecting compromised IoT devices, and moreover, it can detect compromised IoT devices quickly.
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance
The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure.
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e. g., $\ell_{2, 1}$-norm).
Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Anomaly Detection
Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing.
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories
Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications.
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models
In this work, we show that the reconstruction error of diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images, using them as a plausibility score.
Warped Time Series Anomaly Detection
This paper addresses the problem of detecting time series outliers, focusing on systems with repetitive behavior, such as industrial robots operating on production lines. Notable challenges arise from the fact that a task performed multiple times may exhibit different duration in each repetition and that the time series reported by the sensors are irregularly sampled because of data gaps.
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
In this paper, we propose a novel Domain Adaptation Contrastive learning for Anomaly Detection in multivariate time series (DACAD) model to address this issue by combining UDA and contrastive representation learning.
