As there have been some interesting discussions on the alternatives to backpropagation lately (e.g. this reddit thread), I am sharing our latest work just made available on arXiv:

Learning without feedback: Direct random target projection as a feedback-alignment algorithm with layerwise feedforward training

arXiv link – PyTorch code

Summary: Building on feedback-alignment algorithms, we show how to train multi-layer neural networks using random projections of the target vector, which enables layerwise weight updates using only local and feedforward information. The proposed algorithm is called direct random target projection (DRTP). While backpropagation (BP) requires forward and backward weight symmetry (i.e. weight transport problem) and implies update locking before forward and backward passes have been completed, DRTP solves both problems toward higher biological plausibility and low-cost hardware implementation. Indeed, estimating the layerwise loss gradients only requires a label-dependent random vector selection, making adaptive smart sensors and edge computing the ideal applications due to limited power and computing resources. Despite its simplicity, we demonstrate on the MNIST and CIFAR-10 datasets that DRTP performs close to BP, feedback alignment (FA), direct feedback alignment (DFA) algorithms.

The PyTorch code (link above) also includes implementations of FA and DFA.

Feedback is welcome!