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Anomaly Detection

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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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/WaqasSultani/AnomalyDetectionCVPR2018

Real-world Anomaly Detection in Surveillance Videos

https://github.com/Wslsdx/Deep-Learning-Approach-for-Surface-Defect-Detection

Segmentation-Based Deep-Learning Approach for Surface-Defect Detection

https://github.com/Xiaohui9607/f_anogan_pytorch

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

https://github.com/YUL-git/MemAE

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

https://github.com/YeongHyeon/ADAE-TF

Anomaly Detection with Adversarial Dual Autoencoders

https://github.com/YeongHyeon/ConAD

Anomaly Detection With Multiple-Hypotheses Predictions

https://github.com/YeongHyeon/ConAD-PyTorch

Anomaly Detection With Multiple-Hypotheses Predictions

https://github.com/YeongHyeon/GANomaly

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

https://github.com/YeongHyeon/MemAE

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

https://github.com/YeongHyeon/MemAE-TF2

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

https://github.com/YeongHyeon/RIAD-PyTorch

https://github.com/YeongHyeon/Skip-GANomaly

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

https://github.com/YeongHyeon/YeongHyeon

Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise

https://github.com/YeongHyeon/adVAE

adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

https://github.com/YeongHyeon/f-AnoGAN-TF

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

https://github.com/Young-in/ANM-Assignment2-loglizer

Anomaly Detection using Autoencoders in High Performance Computing Systems

https://github.com/YoungGod/DFC

https://github.com/Youssefares/MURA-Abnormality-Detection-in-Musculoskeletal-Radiographs

MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs

https://github.com/ZIYU-DEEP/Understanding-Bias-in-Deep-Anomaly-Detection-PyTorch

Understanding the Effect of Bias in Deep Anomaly Detection

https://github.com/ZIYU-DEEP/deepSAD-custom

Deep Semi-Supervised Anomaly Detection

https://github.com/ZKSI/CumFSel.jl

Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images

https://github.com/ZKSI/CumulantsFeatures.jl

Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images

https://github.com/ZXP-S-works/VAE-for-Temporal-Process

Auto-Encoding Variational Bayes

https://github.com/Zaza-97/EfficientAD

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

https://github.com/Zhang-Zhi-Jie/Pytorch-MSCRED

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

https://github.com/Zhangyanbo/iResNetLab

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/ZhiHanZ/IRIS0-SPAN

https://github.com/a-d-wicklund/ece283_final_project

Auto-Encoding Variational Bayes

https://github.com/aai-institute/pyDVL

https://github.com/aarthipriyar/GAN

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

https://github.com/abc-125/vad

Supervised Anomaly Detection for Complex Industrial Images

https://github.com/abin24/Saliency-detection-toolbox

https://github.com/ablancha/gpsearch

Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring

https://github.com/acciorocketships/setautoencoder

https://github.com/adityabingi/Beta-VAE

Auto-Encoding Variational Bayes

https://github.com/adityakumarakash/VariationalAutoEncoders

Auto-Encoding Variational Bayes

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.