Student Architecture

Student architecture in deep learning focuses on training smaller, more efficient "student" networks to mimic the performance of larger, more complex "teacher" networks. Current research emphasizes efficient knowledge transfer through various teacher-student architectures, often employing techniques like knowledge distillation and semi-supervised learning to leverage both labeled and unlabeled data. This area is crucial for deploying advanced deep learning models on resource-constrained devices and reducing the need for extensive labeled datasets, impacting fields ranging from medical image analysis to natural language processing. Automated architecture search methods are increasingly used to optimize student network design for specific tasks and computational budgets.