https://github.com/titu1994/MLSTM-FCN
LSTM Fully Convolutional Networks for Time Series Classification
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
LSTM Fully Convolutional Networks for Time Series Classification
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.