Module-wise Training of Neural Networks via the Minimizing Movement Scheme

TL;DR

This paper introduces TRGL, a module-wise training method for neural networks that uses a novel regularization inspired by gradient flow schemes, improving accuracy and reducing memory usage in constrained settings.

Contribution

It proposes a new regularization technique based on the minimizing movement scheme, enhancing greedy module-wise training of neural networks.

Findings

Improved accuracy in module-wise training of ResNets, Transformers, VGG.

Achieved up to 60% less memory usage compared to end-to-end training.

Demonstrated superiority over other module-wise methods.

Abstract

Greedy layer-wise or module-wise training of neural networks is compelling in constrained and on-device settings where memory is limited, as it circumvents a number of problems of end-to-end back-propagation. However, it suffers from a stagnation problem, whereby early layers overfit and deeper layers stop increasing the test accuracy after a certain depth. We propose to solve this issue by introducing a module-wise regularization inspired by the minimizing movement scheme for gradient flows in distribution space. We call the method TRGL for Transport Regularized Greedy Learning and study it theoretically, proving that it leads to greedy modules that are regular and that progressively solve the task. Experimentally, we show improved accuracy of module-wise training of various architectures such as ResNets, Transformers and VGG, when our regularization is added, superior to that of other module-wise training methods and often to end-to-end training, with as much as 60% less memory usage.