Frequency Switching Mechanism for Parameter-E!cient Multi-Task Learning
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 th...
| Authors | Shih-Wen Liu, Yen-Chang Chen, Wei-Ta Chu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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