https://github.com/Ars235/Novelty_Detection
Adversarially Learned One-Class Classifier for Novelty Detection
Papers with Code 아카이브
Outlier Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 11
- 보존된 평가 기록
- 13
Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Burf/tfdetection
https://github.com/Chandan-IITI/LMKAD
Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification
https://github.com/Divyanshu-Singh-Chauhan/Digital-Image-Forgery-Detection
Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier
https://github.com/ErikKratzCth/ALOCC_Keras_SMILE
Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux
https://github.com/GuansongPang/deep-outlier-detection
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
https://github.com/JoegameZhou/PatchCore
https://github.com/JohnYKiyo/density_ratio_estimation
Condition Number Analysis of Kernel-based Density Ratio Estimation
https://github.com/KDD-OpenSource/DeepADoTS
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
https://github.com/KarthikKothareddy/pyod
https://github.com/LilitYolyan/CutPaste
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
https://github.com/MVIG-SJTU/RMPE
Multi-Person Pose Estimation with Local Joint-to-Person Associations
https://github.com/MathieuCarriere/perslay
https://github.com/MichauGabriel/HELM
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
https://github.com/MindSpore-paper-code-2/code399/tree/main/LSTM
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
https://github.com/OpenAOI/anodet
https://github.com/PyLink88/RecAE
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
https://github.com/RafaJPSantos/bagged.outliertrees
Explainable outlier detection through decision tree conditioning
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/RezaSadeghiWSU/ASVDD
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/SauravMaheshkar/Compressed-DNNs-Forget
https://github.com/Tony607/ALOCC_Keras
Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/Ultranity/Anomaly.Paddle
https://github.com/ZIYU-DEEP/deepSAD-custom
https://github.com/aai-institute/pyDVL
https://github.com/acciorocketships/setautoencoder
https://github.com/adverml/advopenood
https://github.com/amazon-science/patchcore-inspection
https://github.com/anjith2006/bob.paper.oneclass_mccnn_2019
https://github.com/antoinedemathelin/openood
https://github.com/antonbrazhnyi/AdaLAM
https://github.com/any-tech/PatchCore-ex
https://github.com/arezoosarvi/omit_fair_ranking
Understanding and Mitigating the Effect of Outliers in Fair Ranking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.