Anomaly Detection
1244 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
Most implemented papers
Learning Deep Features for One-Class Classification
We propose a deep learning-based solution for the problem of feature learning in one-class classification.
Adversarially Learned One-Class Classifier for Novelty Detection
khalooei/ALOCC-CVPR2018
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CVPR 2018
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images
In this paper we present an analysis of a general algorithm for band selection based on higher order cumulants.
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
KONI-SZ/MSCRED
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Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
donggong1/memae-anomaly-detection
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ICCV 2019
At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data.
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images.
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
