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arXiv Machine Learning By Yubo Huang, Yushe Cao, Zixi Wang, Jingzehua Xu, Xin Lai, Xiongyu Hu, Shuai Zhang

Learning Domain Trajectories with Flow Matching for Gradual Domain Adaptation

Read the original on arXiv Machine Learning →

The paper introduces FlowGDA, a flow‑matching framework for gradual domain adaptation that models adaptation as continuous transport on a statistical manifold. FlowGDA learns time‑dependent velocity fields to generate smooth domain trajectories, avoiding adversarial Min–Max optimization by using a simulation‑free regression objective. The method incorporates source‑anchored transport, semantic consistency loss, and a two‑stage optimization scheme to prevent semantic drift, achieving superior performance over state‑of‑the‑art techniques.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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