Anomaly Detection
1222 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
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.
CARE to Compare: A real-world dataset for anomaly detection in wind turbine data
Anomaly detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain specific public datasets are scarce.
Anomaly Correction of Business Processes Using Transformer Autoencoder
These methods cannot accurately and efficiently detect anomalies and correct anomalies at the same time, and they all rely on the set threshold to detect anomalies.
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection
In this paper, we present two novel methods in Network Intrusion Detection Systems (NIDS) using Graph Neural Networks (GNNs).
Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection
However, existing approaches depend on static anomaly prompts that are prone to cross-semantic ambiguity, and prioritize global image-level representations over crucial local pixel-level image-to-text alignment that is necessary for accurate anomaly localization.
Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series
Online unsupervised detection of anomalies is crucial to guarantee the correct operation of cyber-physical systems and the safety of humans interacting with them.
Machine learning-based identification of Gaia astrometric exoplanet orbits
The third Gaia data release (DR3) contains $\sim$170 000 astrometric orbit solutions of two-body systems located within $\sim$500 pc of the Sun.
Fault Detection in Mobile Networks Using Diffusion Models
In today's hyper-connected world, ensuring the reliability of telecom networks becomes increasingly crucial.
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora
Both the method and dataset offer the basis for comprehensive empirical research into the concept of media storms, including characterizing them and predicting their outbursts and durations, in mainstream media or social media platforms.
Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling
Hence, this is posed as an anomaly detection task in agricultural images.
