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

• URL: http://arxiv.org/abs/2410.02147v1
• Date: Thu, 3 Oct 2024 02:12:03 GMT
• Title: Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
• Authors: Gaurav Patel, Christopher Sandino, Behrooz Mahasseni, Ellen L Zippi, Erdrin Azemi, Ali Moin, Juri Minxha,
• Abstract summary: We propose a framework for efficient Source-Free Domain Adaptation (SFDA) Our approach introduces an improved paradigm for source-model preparation and target-side adaptation. We demonstrate that our framework is compatible with various SFDA methods and achieves significant computational efficiency.
• Score: 0.7558576228782637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. We demonstrate using PAC Bayesian analysis that this selective fine-tuning strategy implicitly regularizes the adaptation process by constraining the model's learning capacity. Furthermore, this re-parameterization reduces the overall model size and enhances inference efficiency, making the approach particularly well suited for resource-constrained devices. Additionally, we demonstrate that our framework is compatible with various SFDA methods and achieves significant computational efficiency, reducing the number of fine-tuned parameters and inference overhead in terms of MACs by over 90% while maintaining model performance.

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