Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement

Understand

In this paper, we propose a framework for efficient Source-Free Domain Adaptation (SFDA) in the context of time-series, focusing on enhancing both parameter efficiency and data-sample utilization.

• Our approach introduces an improved paradigm for source-model preparation and target-side adaptation, aiming to enhance training efficiency during target adaptation.
• Specifically, we reparameterize the source model's weights in a Tucker-style decomposed manner, factorizing the model into a compact form during the source model preparation phase.
• During target-side adaptation, only a subset of these decomposed factors is fine-tuned, leading to significant improvements in training efficiency.