Weight Sharing

Weight sharing, a technique where multiple parts of a neural network share the same parameters, aims to improve efficiency and performance by reducing model size and computational cost. Current research focuses on applying weight sharing to various architectures, including transformers, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), often within the context of federated learning, continual learning, and neural architecture search. This approach offers significant advantages in resource-constrained environments and large-scale applications, impacting both the efficiency of training and the deployment of deep learning models.