Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
Authors: Gaurav Patel, Christopher M. Sandino, Behrooz Mahasseni, Ellen Zippi, Erdrin Azemi, Ali Moin, Juri Minxha
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that low-rank weight disentanglement during source-model preparation enables parameter-efficient adaptation on the target side, consistently improving performance across various SFDA methods (Liang et al., 2020; Yang et al., 2021a; 2022; Ragab et al., 2023b) and time-series benchmarks (Ragab et al., 2023a;b). |
| Researcher Affiliation | Collaboration | Purdue University , Apple |
| Pseudocode | Yes | Algorithm 1: The higher-order orthogonal iteration (HOOI) algorithm. (De Lathauwer et al., 2000; Kolda & Bader, 2009) Input: Tensor A RI1 I2 IN , Truncation (R1, R2, . . . , RN), Initial guess {U(n) 0 : n = 1, 2, . . . , N} Output: Core tensor G, Factor matrices {U(n) k : n = 1, 2, . . . , N} |
| Open Source Code | No | The paper does not provide an explicit statement of code release or a link to a code repository. It only lists author contact emails. |
| Open Datasets | Yes | We utilize the Ada Time benchmarks proposed by Ragab et al. (2023a;b) to evaluate the SFDA methods: SSC (Goldberger et al., 2000), and MFD (Lessmeier et al., 2016), HHAR (Stisen et al., 2015), UCIHAR (Anguita et al., 2013), WISDM (Kwapisz et al., 2011). |
| Dataset Splits | Yes | Adaptations are conducted using 0.5%, 5%, and 100% of the total unlabeled target samples, randomly sampled in a stratified manner. Table 3 outlines the specific details of each dataset, including the number of domains, sensor channels, class categories, sample lengths, and the total sample count for both training and testing sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The backbone and classifier weights are optimized using the Adam optimizer (Kingma, 2014) with a learning rate of 1e-3. The paper mentions the Adam optimizer and references a paper from 2014, but does not specify software dependencies like specific library versions (e.g., PyTorch version, TensorFlow version, or Python version). |
| Experiment Setup | Yes | The backbone weights are optimized to adapt to the target distribution, with Adam (Kingma, 2014) used as the optimizer to learn the target-adapted weights. We experiment with a range of learning rates: {5e-4, 1e-4, 5e-5, 1e5, 5e-6, 1e-6, 5e-7, 1e-7} for each method (including the baseline) and report the best performance achieved. For all datasets, we utilize a simple 3-layer 1D-CNN backbone following (Ragab et al., 2023b)... Specifically, we set the filter sizes to 25 for SSC, 32 for MFD, 5 for HHAR, 5 for WISDM, and 5 for UCIHAR, following (Ragab et al., 2023a). |