Spectral Fine Tuning

Spectral fine-tuning refines pre-trained deep learning models by adjusting their spectral properties, aiming to improve efficiency and performance in various applications. Current research focuses on incorporating spectral information into parameter-efficient fine-tuning methods, utilizing techniques like singular value decomposition and exploring different model architectures such as conformers and transformers. This approach enhances model adaptability and robustness across diverse tasks, including speech enhancement, image processing (e.g., hyperspectral image recovery and pansharpening), and biomedical signal analysis (e.g., EEG classification), ultimately leading to more efficient and accurate solutions.