Papers with Code 아카이브
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/maxfrenzel/CompressionVAE
https://github.com/mbohlkeschneider/psa-gan
https://github.com/md-mohaiminul/objectstatechange
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mhwong2007/GEE
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
https://github.com/mic-dkfz/bodypartregression
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks
https://github.com/miccunifi/analog-video-restoration
https://github.com/microsoft/recommenders
https://github.com/mindspore-ai/contrib/tree/master/application/pro
https://github.com/mindspore-ai/models/tree/master/official/cv/patchcore
https://github.com/mistic-lab/IPSW-RFI
SAIFE: Unsupervised Wireless Spectrum Anomaly Detection with Interpretable Features
https://github.com/mjack3/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/mmrl/disent-and-gen
https://github.com/mmrl/lost-in-latent-space
https://github.com/mnecipkurt/tsg19
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
https://github.com/modadundun/ProDisc-VAD
https://github.com/moment-timeseries-foundation-model/timeseriesexam
https://github.com/mortezagolzan/Face-Anti-Spoofing
https://github.com/msd-irimas/multi_comparison_matrix
https://github.com/msminhas93/anomaly-detection-using-autoencoders
https://github.com/mulkkyul/stochasticRNNPB
https://github.com/mullue/anogan-mnist
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/munhouiani/MIDAS
https://github.com/musyoku/chainer-glow
https://github.com/musyoku/generative-flow
https://github.com/mx-mark/videotransformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/nachonavarro/seasonal-esd-anomaly-detection
Automatic Anomaly Detection in the Cloud Via Statistical Learning
https://github.com/nakumgaurav/Anomaly-Detection
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
https://github.com/nakumgaurav/Anomaly-Detection_Varitaional-Autoencoders
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
https://github.com/nawta/GSoC_deeplense_assignment
https://github.com/nelson1425/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/nghorbani/human_body_prior
https://github.com/ngiann/ApproximateVI.jl
https://github.com/ngoix/ocrf
https://github.com/nguyetn89/Anomaly_detection_BMVC2019
https://github.com/nguyetn89/Anomaly_detection_ICCV2019
Anomaly Detection in Video Sequence with Appearance-Motion Correspondence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.