End-to-end (E2E) training approaches are commonly plagued by high memory
consumption, reduced efficiency in training, challenges in model
parallelization, and suboptimal biocompatibility. Local learning is considered
a novel interactive training method that holds promise as an alternative to
E2E. Nonetheless, conventional local learning methods fall short in achieving
high model accuracy due to inadequate local inter-module interactions. In this
paper, we introduce a new model known as the Scaling Supervised Local Learning
with Multilaminar Leap Augmented Auxiliary Network (MLAAN). MLAAN features an
innovative supervised local learning approach coupled with a robust
reinforcement module. This dual-component design enables the MLAAN to integrate
smoothly with established local learning techniques, thereby enhancing the
efficacy of the foundational methods. The method simultaneously acquires the
local and global features of the model separately by constructing an
independent auxiliary network and a cascade auxiliary network on the one hand
and incorporates a leap augmented module, which serves to counteract the
reduced learning capacity often associated with weaker supervision. This
architecture not only augments the exchange of information amongst the local
modules but also effectively mitigates the model's tendency toward myopia. The
experimental evaluations conducted on four benchmark datasets, CIFAR-10,
STL-10, SVHN, and ImageNet, demonstrate that the integration of MLAAN with
existing supervised local learning methods significantly enhances the original
methodologies. Of particular note, MLAAN enables local learning methods to
comprehensively outperform end-to-end training approaches in terms of optimal
performance while saving GPU memory.

Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network (MLAAN) integrates smoothly with established local learning techniques, enhances efficacy, improves model accuracy, and outperforms end-to-end training approaches while saving GPU memory.