Papers with Code 아카이브
Outlier Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 11
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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/jakerhodes/RF-GAP-R
https://github.com/jmmartyn/neural-network-quantum-field-states
https://github.com/joachimschreurs/Outlier-detection-in-non-elliptical-data-by-kernel-MRCD
https://github.com/kclip/r_autoeval_plus
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
https://github.com/kevinmoonlab/rf-gap
https://github.com/kevinwss/Deep-SAD-Baseline
https://github.com/kishordgupta/pyod
https://github.com/kit-dbis/ocal-evaluation
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers
https://github.com/kmutya/Algorithms-for-Drowsy-Driver-Detection
LSTM Fully Convolutional Networks for Time Series Classification
https://github.com/kzkadc/alocc_mnist
Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/liuyilin950623/SHAP_on_Autoencoder
https://github.com/lmunoz-gonzalez/Poisoning-Attacks-with-Back-gradient-Optimization
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
https://github.com/lwtnn/lwtnn
https://github.com/m-3lab/awesome-visual-sensory-anomaly-detection
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
https://github.com/mansimane/zslearning_baseline
https://github.com/marclafon/heatood
SSD: A Unified Framework for Self-Supervised Outlier Detection
https://github.com/mganjoo/zslearning
https://github.com/mindspore-ai/models/tree/master/official/cv/patchcore
https://github.com/mnecipkurt/tsg19
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach
https://github.com/openvinotoolkit/anomalib
https://github.com/pb-brainiac/semseg_od
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
https://github.com/pg2455/AudioAge
https://github.com/phuijse/MATIC
LSTM Fully Convolutional Networks for Time Series Classification
https://github.com/pluskal-lab/massspecgym
https://github.com/proy3/Abnormal_Trajectory_Classifier
Road User Abnormal Trajectory Detection using a Deep Autoencoder
https://github.com/pylink88/recurrent-autoencoder
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
https://github.com/qarchli/pytorch-gan-for-outlier-detection
Generative Adversarial Active Learning for Unsupervised Outlier Detection
https://github.com/rekords-uw/Picket
Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference
https://github.com/roytalman/LSTM-FCN-Pytorch
LSTM Fully Convolutional Networks for Time Series Classification
https://github.com/rvorias/ind_knn_ad
https://github.com/sooonwoo/RotNet-OOD
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
https://github.com/taikiinoue45/PatchCore
https://github.com/tbcvContributor/DeepHawkeye
https://github.com/timeseriesAI/tsai/tree/main/tsai/models
LSTM Fully Convolutional Networks for Time Series Classification
https://github.com/tiskw/patchcore-ad
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.