Forward Forward

Forward-forward (FF) algorithms represent a novel approach to training neural networks, offering an alternative to the ubiquitous backpropagation method. Current research focuses on adapting FF to various architectures, including convolutional neural networks (CNNs) and transformers, and improving efficiency and performance through techniques like self-adaptation and optimized loss functions. This research aims to address limitations of backpropagation, such as high computational cost and difficulty in training very deep networks, potentially leading to more resource-efficient and scalable machine learning models for diverse applications, including medical image analysis and self-driving systems.