Model Reprogramming

Model reprogramming is a machine learning technique that adapts pre-trained models to new tasks without altering their parameters, achieving resource-efficient cross-domain learning. Current research focuses on applying this approach to diverse areas, including image and speech processing, graph neural networks, and assistive technologies, often outperforming traditional fine-tuning methods in scenarios with limited data or computational resources. This technique offers significant potential for improving the efficiency and accessibility of machine learning across various domains, particularly in resource-constrained settings where training from scratch is impractical.