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Contrastive Time-Series Anomaly Detection
Hyun Gi Kim
, Siwon Kim
, Seonwoo Min
,
Byunghan Lee
Electronic Engineering Program
Seoul National University
LG AI Research
Research output
:
Contribution to journal
›
Article
›
peer-review
12
Scopus citations
Overview
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Computer Science
Anomaly Detection
100%
Data Augmentation
60%
Time Series Data
60%
Multivariate Time Series
40%
Contrastive Loss
30%
Detection Performance
20%
Contrastive Learning
20%
Lstm
10%
World Application
10%
Representation Learning
10%
Temporal Context
10%
Outstanding Performance
10%
Earth and Planetary Sciences
Time Series
100%