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


Use these libraries to find Anomaly Detection models and implementations
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Most implemented papers

Auto-Encoding Variational Bayes

microsoft/recommenders 20 Dec 2013

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

openvinotoolkit/anomalib 25 Mar 2023

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

LeeDoYup/AnoGAN 17 Mar 2017

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

openvinotoolkit/anomalib 17 Nov 2020

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

amazon-research/patchcore-inspection CVPR 2022

Being able to spot defective parts is a critical component in large-scale industrial manufacturing.

DeepWalk: Online Learning of Social Representations

PaddlePaddle/PaddleRec 26 Mar 2014

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

hendrycks/error-detection 7 Oct 2016

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?

facebookresearch/TimeSformer 9 Feb 2021

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

pyaf/DenseNet-MURA-PyTorch 11 Dec 2017

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

chickenbestlover/RNN-Time-series-Anomaly-Detection 1 Jul 2016

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine.