Backpropagation Free

Backpropagation-free training aims to develop deep learning methods that avoid the computationally expensive and biologically implausible backpropagation algorithm. Current research focuses on alternative training strategies, such as direct feedback alignment, forward-forward methods, zeroth-order optimization, and biologically-inspired learning rules, often applied within specific architectures like spiking neural networks or graph neural networks. These efforts seek to improve training efficiency, reduce energy consumption, and enhance the biological plausibility of artificial neural networks, with potential applications ranging from resource-constrained edge devices to more efficient large-scale models. The ultimate goal is to create equally effective, or even superior, alternatives to backpropagation for various deep learning tasks.