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An implementation of the SIGKDD 2024 paper--Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution Classification.

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ITSR

An implementation of the SIGKDD 2024 paper--Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution Classification.

Dataset

The three datasets used in the paper (UCIHAR, UniMiB SHAR and Opportunity) can be downloaded from here, while the remaining EMG dataset is sourced from here. Afterward, the datasets will be divided according to different individuals. Please place the downloaded datasets in the ../data.

Usage

To conduct the experiments, please execute main.py. When running, select the dataset (select from UCIHAR, Uni, EMG, and Oppo.) and target domain. Additional hyperparameters can be explored within the main.py. Here's an example.

python main.py --dataset UCIHAR --target_domain 0 --out_channel 16

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An implementation of the SIGKDD 2024 paper--Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution Classification.

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