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Anomaly Detection

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하위 태스크: 28
평가표: 76
보존된 평가 기록: 669

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/travishsu/ReConPatch-TF

ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

https://github.com/trungnt13/odin-ai

Auto-Encoding Variational Bayes

https://github.com/tuananh0305/GCN_ANOMALY_DETECTION

Semi-supervised Anomaly Detection on Attributed Graphs

https://github.com/tungkw/VAE

Auto-Encoding Variational Bayes

https://github.com/tusharsarkar3/XBNet

XBNet : An Extremely Boosted Neural Network

https://github.com/txyugood/Knowledge_Distillation_AD_Paddle

Multiresolution Knowledge Distillation for Anomaly Detection

https://github.com/ubc-vision/mist

MIST: Multiple Instance Spatial Transformer Network

https://github.com/ucbtns/medgan

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

https://github.com/unit8co/darts

Long-term Forecasting with TiDE: Time-series Dense Encoder

https://github.com/ushashwat/MURA-Bone-Abnormality-Detection

MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs

https://github.com/uzl/inpainting-transformer

Inpainting Transformer for Anomaly Detection

https://github.com/verivital/nnv

Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input

https://github.com/victordibia/deepad

Deep Learning for Anomaly Detection: A Survey

https://github.com/vignesh-creator/Variational-Autoencoder

Auto-Encoding Variational Bayes

https://github.com/vimlord/torchrosa-tts

Auto-Encoding Variational Bayes

https://github.com/vincekellner/demandforecasting

Deep and Confident Prediction for Time Series at Uber

https://github.com/vitskvara/GenModels.jl

Auto-Encoding Variational Bayes

https://github.com/vitskvara/GenerativeModels.jl

Auto-Encoding Variational Bayes

https://github.com/vlievin/biva-pytorch

BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

https://github.com/vsatyakumar/automatic-local-outlier-factor-tuning

Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection

https://github.com/vt-le/astnet

https://github.com/vvvm23/glow

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/vxld014/S-H-ESD

Automatic Anomaly Detection in the Cloud Via Statistical Learning

https://github.com/waico/SKAB/blob/master/notebooks/ArimaFD.ipynb

https://github.com/waico/arimafd

https://github.com/waico/experiment_arimafd

https://github.com/wanboyang/Anomaly_AR_Net_ICME_2020

Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning

https://github.com/wasve/DRAEM-SSPCAB

Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

https://github.com/wen-yan-lin/LVAD-Locally-Varying-Anomaly-Detection

https://github.com/wen-yan-lin/shell-theory

https://github.com/wgopar/VariationalAutoencoder

Auto-Encoding Variational Bayes

https://github.com/wikibook/keras

Auto-Encoding Variational Bayes

https://github.com/williamcfrancis/variational-autoencoder-for-mnist

Auto-Encoding Variational Bayes

https://github.com/williamleif/GraphSAGE

DeepWalk: Online Learning of Social Representations

https://github.com/winfredge/t2s

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

https://github.com/winstonll/SynC

PyOD: A Python Toolbox for Scalable Outlier Detection

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