We focus on a class of local learning rules, called local-SSL, that optimize self-supervised objectives at each layer rather than at the final output layer.
Gradients are detached between layers, so no backward pass is needed. Examples are CLAPP(Illing et al. 2021) and Forward-Forward algorithms.
