Papers with Code 아카이브
Outlier Detection
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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/winstonll/COPOD
공식 코드https://github.com/wogur110/PNI_anomaly_detection
공식 코드PNI : Industrial Anomaly Detection using Position and Neighborhood Information
https://github.com/xaggi/OGNet
공식 코드Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
https://github.com/xiaofeng-tan/mgbod
공식 코드Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls
https://github.com/xuhongzuo/couta
공식 코드Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
https://github.com/xycheng/EmbeddingNorm
공식 코드Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian
https://github.com/xyvivian/hyper
공식 코드Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
https://github.com/xyvivian/robod
공식 코드https://github.com/yangarbiter/nearest-category-generalization
공식 코드https://github.com/yaoshuwang/SelNet-Estimation
공식 코드Consistent and Flexible Selectivity Estimation for High-Dimensional Data
https://github.com/yinglunz/ROAI_ICML2020
공식 코드https://github.com/yue-fan/ssb
공식 코드SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
https://github.com/yufeiwu1011/Robust-Amortized-Bayesian-Inference
공식 코드Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models
https://github.com/yuyang16101066/anedl
공식 코드Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning
https://github.com/yuyongcan/stamp
공식 코드STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
https://github.com/yzhao062/DCSO
공식 코드DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles
https://github.com/yzhao062/LSCP
공식 코드LSCP: Locally Selective Combination in Parallel Outlier Ensembles
https://github.com/yzhao062/SUOD
공식 코드https://github.com/yzhao062/XGBOD
공식 코드XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning
https://github.com/yzhao062/elect
공식 코드https://github.com/yzhao062/metaod
공식 코드https://github.com/yzhao062/pyod
공식 코드https://github.com/yzhao062/pytod
공식 코드TOD: GPU-accelerated Outlier Detection via Tensor Operations
https://github.com/zhliu-uod/multi-t
공식 코드Rethinking Unsupervised Outlier Detection via Multiple Thresholding
https://github.com/zibrahim/KD-OP
공식 코드https://github.com/zifengcheng/slmm
공식 코드Learning to Classify Open Intent via Soft Labeling and Manifold Mixup
https://github.com/ziyiliang/conformalized_early_stopping
공식 코드Conformal inference is (almost) free for neural networks trained with early stopping
https://github.com/zzwjames/dpgba
공식 코드Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
https://gitlab.com/artelabsuper/ocdmst
공식 코드Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
https://gitlab.com/bardhp95/bae
공식 코드Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
https://gitlab.com/bpaassen/ast2vec
공식 코드ast2vec: Utilizing Recursive Neural Encodings of Python Programs
https://github.com/2023-MindSpore-1/ms-code-223/tree/main/SSD
SSD: A Unified Framework for Self-Supervised Outlier Detection
https://github.com/2023-MindSpore-1/ms-code-4/tree/main/PatchCore
https://github.com/AI-for-Ocean-Science/ulmo
https://github.com/AdityaGolatkar/Sparse-Kernel-PCA-for-outlier-detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.