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Anomaly Detection

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하위 태스크: 28
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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/daniel-hain/ML_course_maastricht

Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough Patents

https://github.com/danielenricocahall/One-Class-NeuralNetwork

Anomaly Detection using One-Class Neural Networks

https://github.com/danielsoy/EfficientAD

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

https://github.com/danielsoy/EfficientAD-1

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

https://github.com/danieltan07/dagmm

https://github.com/danyleb/variational-lstm-autoencoder

A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder

https://github.com/datamllab/tods

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

https://github.com/davide-coccomini/TimeSformer-Video-Classification

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

https://github.com/davidemartinelli/VAE

Auto-Encoding Variational Bayes

https://github.com/davidshih17/ANODE

Anomaly Detection with Density Estimation

https://github.com/dddb11/MVSS-Net

Image Manipulation Detection by Multi-View Multi-Scale Supervision

https://github.com/decisionintelligence/pathformer

Transformers in Time Series: A Survey

https://github.com/dellysursh/EfficientAD

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

https://github.com/denguir/student-teacher-anomaly-detection

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings

https://github.com/desimone/Musculoskeletal-Radiographs-Abnormality-Classifier

MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs

https://github.com/devbas/ovassistant-alpha

Spatio-Temporal Data Mining: A Survey of Problems and Methods

https://github.com/dhsvendsen/rtm_vi_mcem_inference

Auto-Encoding Variational Bayes

https://github.com/dineshssdn-867/Anomaly-Detection-CVPR2018

Real-world Anomaly Detection in Surveillance Videos

https://github.com/dl4mhealth/adformer

ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease Assessment

https://github.com/dmadiad/DMAD

DMAD: Dual Memory Bank for Real-World Anomaly Detection

https://github.com/dmlc/dgl/tree/master/examples/pytorch/caregnn

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/donggong1/memae-anomaly-detection

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

https://github.com/drumpt/RotNet-OOD

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

https://github.com/ds-academy/TadGAN

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

https://github.com/dulpy/ecva

Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly

https://github.com/dungxibo123/vae

Auto-Encoding Variational Bayes

https://github.com/dutxiaoli/Vae_for_Photon-counting

Auto-Encoding Variational Bayes

https://github.com/ebagdasa/backdoors101

How To Backdoor Federated Learning

https://github.com/ee357-computer-network/FinancialCrisisAL

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

https://github.com/ehsannowroozi/RDFS

Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples

https://github.com/ekosman/AnomalyDetectionCVPR2018-Pytorch

Real-world Anomaly Detection in Surveillance Videos

https://github.com/elenaehrlich/gluon-ts

GluonTS: Probabilistic Time Series Models in Python

https://github.com/elisejiuqizhang/USAD-on-WADI-and-SWaT

https://github.com/enalisnick/stick-breaking_dgms

Auto-Encoding Variational Bayes

https://github.com/ensta-u2is/deeplabv3plus-muad-pytorch

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/epfl-lts2/sparkwiki

Anomaly detection in the dynamics of web and social networks

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