Anomaly Detection
1007 papers with code • 59 benchmarks • 78 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
Libraries
Use these libraries to find Anomaly Detection models and implementationsSubtasks
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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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Image Manipulation Detection
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Graph Anomaly Detection
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Abnormal Event Detection In Video
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Weakly-supervised Anomaly Detection
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Self-Supervised 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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Group Anomaly Detection
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3D Anomaly Detection
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Depth Anomaly Detection and Segmentation
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RGB+Depth Anomaly Detection and Segmentation
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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
Most implemented papers
Auto-Encoding Variational Bayes
First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods.
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
We train a student network to predict the extracted features of normal, i. e., anomaly-free training images.
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting.
Towards Total Recall in Industrial Anomaly Detection
Being able to spot defective parts is a critical component in large-scale industrial manufacturing.
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of vertices in a network.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
We consider the two related problems of detecting if an example is misclassified or out-of-distribution.
Is Space-Time Attention All You Need for Video Understanding?
We present a convolution-free approach to video classification built exclusively on self-attention over space and time.
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.