Channel Wise Lightweight Reprogramming

Channel-wise lightweight reprogramming adapts pre-trained deep learning models to new tasks by modifying their inputs or adding small, task-specific modules, rather than extensive retraining. Current research focuses on improving efficiency and performance in various domains, including spatio-temporal forecasting, vision transformers, and continual learning, often employing techniques like Fourier transforms or selective block fine-tuning. This approach offers significant advantages in resource-constrained environments and scenarios with limited data, potentially impacting diverse fields by enabling efficient model adaptation and reducing the computational cost of deploying AI systems.