Forward Forward Algorithm

The Forward-Forward (FF) algorithm offers a biologically-inspired alternative to backpropagation for training neural networks, aiming to improve efficiency and reduce computational demands by using only forward passes. Current research focuses on enhancing FF's performance and generalizability through techniques like contrastive learning, self-supervised learning, and modifications to the loss function, often applied to convolutional neural networks and recurrent neural networks. This approach holds significance for resource-constrained applications, such as on-chip learning and federated learning, and offers potential insights into biological neural network learning mechanisms.