<ul><li>Universal Domain Adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, even when their classes are not fully shared.</li><li>Few dedicated UniDA methods exist for Time Series (TS), which remains a challenging case.</li><li>The paper introduces UniJDOT, an optimal-transport-based method that accounts for the unknown target samples in the transport cost.</li><li>Experiments on TS benchmarks demonstrate the discriminability, robustness, and state-of-the-art performance of UniJDOT.</li></ul>

Optimal Transport and Adaptive Thresholding for Universal Domain Adaptation on Time Series

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