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/RonyAbecidan/Mantranet-pytorch
https://github.com/Rthete/STPM-mindspore
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/Runinho/pytorch-cutpaste
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
https://github.com/Ryosaeba8/Anomaly_detection
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
https://github.com/SKvtun/MSCRED-Pytorch
https://github.com/SashaMalysheva/Pytorch-VAE
https://github.com/ShreyasArthur/AbnormalEventDetection
https://github.com/ShrishtiHore/Anomaly-Detection-in-CCTV-Surveillance-Videos
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
https://github.com/SimonThomine/DistillationAD
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/SimonThomine/RememberingNormality
Student-Teacher Feature Pyramid Matching for Anomaly Detection
https://github.com/SkiddieAhn/Code-WinCLIP
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
https://github.com/SkiddieAhn/Paper-Making-Anomalies-More-Anomalous
https://github.com/SohanLalYadav2304/Anomaly-detection-via-oversampling-principal-component-analysis
https://github.com/StephenPauwels/edbn
BINet: Multi-perspective Business Process Anomaly Classification
https://github.com/StephenPauwels/edbn_ecmlpkdd
BINet: Multi-perspective Business Process Anomaly Classification
https://github.com/StevenLiuWen/sRNN_TSC_Anomaly_Detection
https://github.com/SudeepSarkar/matlabHTM
https://github.com/SunnyHaze/ManTraNet-Pytorch
https://github.com/SuyashSonawane/fakedetector
https://github.com/TaikiInoue/STAD
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
https://github.com/TanyaChutani/VAE-TF2.0
https://github.com/TeCSAR-UNCC/SPARTA
https://github.com/The-AI-Summer/self-attention-cv
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/ThinhNgVhust/ocsvm
https://github.com/Tony607/ALOCC_Keras
Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/TooTouch/MemSeg
https://github.com/Ultranity/Anomaly.Paddle
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
https://github.com/UnofficialJuliaMirror/CumulantsFeatures.jl-89efba0d-c40c-5510-8345-5c0ed49e5930
https://github.com/UnofficialJuliaMirrorSnapshots/CumulantsFeatures.jl-89efba0d-c40c-5510-8345-5c0ed49e5930
https://github.com/UsmannK/TABOR
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems
https://github.com/VealFang/EfficientAD_realdata
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/Vikrant7981/anomaly_detection
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection
https://github.com/WLM1ke/poptimizer
https://github.com/WS-W/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/WangFengJiee/MiniMaxAD
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.