Transferable Neural

Transferable neural networks aim to leverage pre-trained models for new tasks, reducing the need for extensive retraining and data. Current research focuses on improving transferability across diverse domains, including image reconstruction, hardware implementation, and solving partial differential equations, employing architectures like convolutional neural networks, graph convolutional networks, and U-Nets, often incorporating techniques like regularization and domain adaptation. This research is significant because it enhances efficiency and robustness in various applications, ranging from medical imaging and resource management in wireless networks to autonomous systems and robotics. The resulting models offer improved accuracy and generalization capabilities compared to training from scratch.