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Poster
Module-wise Training of Neural Networks via the Minimizing Movement Scheme
Skander Karkar · Ibrahim Ayed · Emmanuel de Bézenac · Patrick Gallinari

Tue Dec 12 03:15 PM -- 05:15 PM (PST) @ Great Hall & Hall B1+B2 #504
in Poster Session 2 »

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 simple 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.

Author Information

Skander Karkar (ISIR, UMR 7222)
Ibrahim Ayed (Sorbonne Université)
Emmanuel de Bézenac (ETH Zürich)
Patrick Gallinari (Sorbonne Universite, Criteo AI Lab)

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