Module-wise Training of Neural Networks via the Minimizing Movement Scheme
This addresses memory constraints in on-device and limited-resource settings by enabling more efficient neural network training without sacrificing accuracy.
The paper tackles the stagnation problem in greedy module-wise training of neural networks, where early layers overfit and deeper layers stop improving test accuracy, by introducing a transport-regularized greedy learning (TRGL) method. The result is improved accuracy across various architectures like ResNets and Transformers, often outperforming end-to-end training with up to 60% less memory usage.
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.