Paper Detail
Shih-Wen Liu, Yen-Chang Chen, Wei-Ta Chu, Fu-En Yang, Yu-Chiang Frank Wang
Multi-task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited to single-task adaptation. We introduce \textbf{Free Sinewich}, a parameter-efficient multi-task learning framework that enables near-zero-cost weight modulation via frequency switching (\textbf{Free}). Specifically, a \textbf{Sine-AWB (Sinewich)} layer combines low-rank factors and convolutional priors into a single kernel, which is then modulated elementwise by a sinusoidal transformation to produce task-specialized weights. A lightweight Clock Net is introduced to produce bounded frequencies that stabilize this modulation during training. Theoretically, sine modulation enhances the rank of low-rank adapters, while frequency separation decorrelates the weights of different tasks. On dense prediction benchmarks, Free Sinewich achieves state-of-the-art performance-efficiency trade-offs (e.g., up to +5.39\% improvement over single-task fine-tuning with only 6.53M trainable parameters), offering a compact and scalable paradigm based on frequency-based parameter sharing. Project page: \href{https://casperliuliuliu.github.io/projects/Free-Sinewich/}{https://casperliuliuliu.github.io/projects/Free-Sinewich}.
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@article{liu2026frequency,
title = {Frequency Switching Mechanism for Parameter-E!cient Multi-Task Learning},
author = {Shih-Wen Liu and Yen-Chang Chen and Wei-Ta Chu and Fu-En Yang and Yu-Chiang Frank Wang},
year = {2026},
abstract = {Multi-task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited to single-task adaptation. We introduce \textbackslash{}textbf\{Free Sinewich\}, a parameter-efficient multi-task learning framework that enables near-zero-cost weight modulation via frequency switching (\textbackslash{}textbf\{Free\}). Specifically, a \textbackslash{}textbf\{Sine-AWB (Sinewich)\} layer combines low-rank factors and convolutional priors into a single k},
url = {https://arxiv.org/abs/2603.21111},
keywords = {cs.CV, cs.LG},
eprint = {2603.21111},
archiveprefix = {arXiv},
}
{}