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Anomaly Detection

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하위 태스크: 28
평가표: 76
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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/lincellularneuroscience/vame

Auto-Encoding Variational Bayes

https://github.com/lironber/GOAD

Classification-Based Anomaly Detection for General Data

https://github.com/lisaong/hss

Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE

https://github.com/liutongkun/EfficientAD

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

https://github.com/liuyilin950623/SHAP_on_Autoencoder

Explaining Anomalies Detected by Autoencoders Using SHAP

https://github.com/llien30/AnoGAN

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

https://github.com/llien30/EfficientGAN-based-anomaly-detection

Efficient GAN-Based Anomaly Detection

https://github.com/llien30/GANomaly

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

https://github.com/llllxt/oneclass-deep-feature

Learning Deep Features for One-Class Classification

https://github.com/lmunoz-gonzalez/Poisoning-Attacks-with-Back-gradient-Optimization

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

https://github.com/logpai/loglizer

Anomaly Detection using Autoencoders in High Performance Computing Systems

https://github.com/logpai/logparser

Self-Supervised Log Parsing

https://github.com/lorenzomammana/EfficientAD

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

https://github.com/louisYen/S3R

https://github.com/lsindoni/VAEMusings

Auto-Encoding Variational Bayes

https://github.com/lucidrains/TimeSformer-pytorch

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/lukasbommes/pv-hawk

Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning

https://github.com/lukasruff/Deep-SVDD-PyTorch

https://github.com/luke-97/BayesByBackprop

Auto-Encoding Variational Bayes

https://github.com/lunpin1101/acobe

Time-Window Group-Correlation Support vs. Individual Features: A Detection of Abnormal Users

https://github.com/lunpin1101/dablog

Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder

https://github.com/lupalab/flowscan

BRUNO: A Deep Recurrent Model for Exchangeable Data

https://github.com/lwtnn/lwtnn

https://github.com/lyeoni/keras-mnist-VAE

Auto-Encoding Variational Bayes

https://github.com/lyeoni/pytorch-mnist-VAE

Auto-Encoding Variational Bayes

https://github.com/m-bain/video-transformers

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/mahkons/flows

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/mangushev/mtad-gat

Multivariate Time-series Anomaly Detection via Graph Attention Network

https://github.com/manigalati/quality_control_CMR

Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation

https://github.com/manigalati/usad

https://github.com/marclafon/heatood

SSD: A Unified Framework for Self-Supervised Outlier Detection

https://github.com/mariegadeberg/AISOutlierDetection

GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

https://github.com/marrrcin/rsrlayer-pytorch

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

https://github.com/marty90/netlytics

Automatic Anomaly Detection in the Cloud Via Statistical Learning

https://github.com/masoudpz/AVID-Adversarial-Visual-Irregularity-Detection

AVID: Adversarial Visual Irregularity Detection

https://github.com/master-plc/fredf

Are Transformers Effective for Time Series Forecasting?

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