Model Stitching

Model stitching is a technique that combines parts of pre-trained neural networks to create new models tailored to specific tasks or resource constraints. Current research focuses on improving stitching methods for diverse applications, including image stitching for panoramic views, federated learning across heterogeneous devices, and zero-shot transfer across languages. This approach offers significant advantages in efficiency, flexibility, and resource management, impacting areas such as computer vision, natural language processing, and medical imaging by enabling the creation of customized models without extensive retraining from scratch.