  A commonly cited inefficiency of neural network training using
back-propagation is the update locking problem: each layer must wait for the
signal to propagate through the full network before updating. Several
alternatives that can alleviate this issue have been proposed. In this context,
we consider a simple alternative based on minimal feedback, which we call
Decoupled Greedy Learning (DGL). It is based on a classic greedy relaxation of
the joint training objective, recently shown to be effective in the context of
Convolutional Neural Networks (CNNs) on large-scale image classification. We
consider an optimization of this objective that permits us to decouple the
layer training, allowing for layers or modules in networks to be trained with a
potentially linear parallelization. With the use of a replay buffer we show
that this approach can be extended to asynchronous settings, where modules can
operate and continue to update with possibly large communication delays. To
address bandwidth and memory issues we propose an approach based on online
vector quantization. This allows to drastically reduce the communication
bandwidth between modules and required memory for replay buffers. We show
theoretically and empirically that this approach converges and compare it to
the sequential solvers. We demonstrate the effectiveness of DGL against
alternative approaches on the CIFAR-10 dataset and on the large-scale ImageNet
dataset.
