Papers with Code 아카이브
Outlier Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/leibinghe/GAAL-based-outlier-detection
공식 코드Generative Adversarial Active Learning for Unsupervised Outlier Detection
https://github.com/lilygeorgescu/AED
공식 코드A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
https://github.com/liznerski/fcdd
공식 코드https://github.com/lohrera/comadout
공식 코드CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD
https://github.com/lohrera/gadformer
공식 코드GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories
https://github.com/lucasponteslpa/QOCClassifier
공식 코드Quantum One-class Classification With a Distance-based Classifier
https://github.com/lukasruff/Deep-SAD-PyTorch
공식 코드https://github.com/mChataign/smileCompletion
공식 코드https://github.com/mala-lab/ggad
공식 코드https://github.com/martiansideofthemoon/ai-detection-paraphrases
공식 코드Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
https://github.com/matejgrcic/Open-set-M2F
공식 코드Outlier detection by ensembling uncertainty with negative objectness
https://github.com/matejgrcic/Vistas-NP
공식 코드Dense open-set recognition with synthetic outliers generated by Real NVP
https://github.com/mattochal/enhanced-outlier-logits
공식 코드EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits
https://github.com/meshiba/robust-conformal-od
공식 코드Robust Conformal Outlier Detection under Contaminated Reference Data
https://github.com/mikeadamer/mag-metric
공식 코드https://github.com/mikkoim/taxonomist-studio
공식 코드https://github.com/minqi824/adbench
공식 코드https://github.com/mmunibas/outlier
공식 코드Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
https://github.com/msesia/conditional-conformal-pvalues
공식 코드https://github.com/mxtsai/ransac-nn
공식 코드https://github.com/naiftt/spafd
공식 코드Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
https://github.com/ncbi-nlp/COVID-19-CT-CXR
공식 코드https://github.com/nishithkumarpaul/robustscaling
공식 코드https://github.com/outlierdetectionjl/outlierdetection.jl
공식 코드OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language
https://github.com/pawni/sgld_online_approximation
공식 코드Efficient variational Bayesian neural network ensembles for outlier detection
https://github.com/poclecoqq/slr-datacleaning
공식 코드Data Cleaning and Machine Learning: A Systematic Literature Review
https://github.com/podgorskiy/GPND
공식 코드Generative Probabilistic Novelty Detection with Adversarial Autoencoders
https://github.com/porscheofficial/distribution_and_volume_based_isolation_forest
공식 코드https://github.com/prprakash02/Importance-Sampling-for-Nonlinear-Models
공식 코드https://github.com/pygod-team/pygod
공식 코드https://github.com/ra1demmmm/autoencoder-based-outlier-detection
공식 코드MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction
https://github.com/radacha/tle
공식 코드Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis
https://github.com/ramkikannan/outliernmf
공식 코드https://github.com/realfolkcode/graphdiffusionanomaly
공식 코드Anomaly Detection in Networks via Score-Based Generative Models
https://github.com/rithram/flynn
공식 코드Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.