[Time-series 논문 리뷰] USAD: UnSupervised Anomaly Detection on Multivariate Time Series(KDD, 2020)

2024 딥러닝/Time-series 논문 리뷰

융딩2 2024. 4. 24. 17:21

Abstract

제안하는 것
- 오토인코더 구조는 비감독 방식으로 학습할 수 있는 능력 O
- 적대적 훈련과 그 구조의 사용은 빠른 훈련을 제공하면서 이상을 분리

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[Time-series 논문 리뷰] A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data(AAAI, 2019) (0)	2024.04.24
[Time-series 논문 리뷰] FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection(WSDM, 2021) (0)	2024.04.24
[Time-series 논문 리뷰] Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles(PVLDB, 2022) (0)	2024.04.24
[Time-series 논문 리뷰] Prototype-oriented unsupervised anomaly detection for multivariate time series(ICML, 2023) (0)	2024.04.24

현재글[Time-series 논문 리뷰] USAD: UnSupervised Anomaly Detection on Multivariate Time Series(KDD, 2020)

LLM, domain adaptation, 딥러닝, multivariate, unsupervised anomaly detection, Sleep Quality, Self-distillation, 딥러닝 개념, forecasting, time-series, Knowledge Distillation, Time Series, fault diagnosis, Video, 시계열 이상탐지, source-free, Outlier Detection, EECS 498-007 / 598-005, time series forecasting, anomaly detection,