Disentangling Domain and General Representations for Time Series Classification

CADT studies unsupervised domain adaptation for time-series classification by explicitly separating domain-invariant representations from domain-specific ones. A class-wise hypersphere objective improves the decision margin of the transferable representation, while domain-preserving augmentations help capture domain-specific patterns. The framework was evaluated on public datasets and multiple real-world applications.

Recommended citation: Youmin Chen, Xinyu Yan, Yang Yang, Jianfeng Zhang, Jing Zhang, Lujia Pan, and Juren Li. Disentangling Domain and General Representations for Time Series Classification. IJCAI 2024.
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