Anomaly Detection

681 papers with code • 36 benchmarks • 55 datasets

Humans are able to detect heterogeneous or unexpected patterns in a set of homogeneous natural images. This task is known as anomaly or novelty detection and has a large number of applications. Anomaly detection automation would enable constant quality control by avoiding reduced attention span and facilitating human operator work.

Anomaly detection is a binary classification between the normal and the anomalous classes. However, it is not possible to train a model with full supervision for this task because we frequently lack anomalous examples, and, what is more, anomalies can have unexpected patterns.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems


Use these libraries to find Anomaly Detection models and implementations

Most implemented papers

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

tSchlegl/f-AnoGAN 17 Mar 2017

Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.

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.

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.

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.

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.

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.

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.

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

korepwx/donut 12 Feb 2018

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

openvinotoolkit/anomalib 17 May 2018

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).

Real-world Anomaly Detection in Surveillance Videos

WaqasSultani/AnomalyDetectionCVPR2018 CVPR 2018

To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.