  As deep neural networks are growing in size and being increasingly deployed
to more resource-limited devices, there has been a recent surge of interest in
network pruning methods, which aim to remove less important weights or
activations of a given network. A common limitation of most existing pruning
techniques, is that they require pre-training of the network at least once
before pruning, and thus we can benefit from reduction in memory and
computation only at the inference time. However, reducing the training cost of
neural networks with rapid structural pruning may be beneficial either to
minimize monetary cost with cloud computing or to enable on-device learning on
a resource-limited device. Recently introduced random-weight pruning approaches
can eliminate the needs of pretraining, but they often obtain suboptimal
performance over conventional pruning techniques and also does not allow for
faster training since they perform unstructured pruning. To overcome their
limitations, we propose Set-based Task-Adaptive Meta Pruning (STAMP), which
task-adaptively prunes a network pretrained on a large reference dataset by
generating a pruning mask on it as a function of the target dataset. To ensure
maximum performance improvements on the target task, we meta-learn the mask
generator over different subsets of the reference dataset, such that it can
generalize well to any unseen datasets within a few gradient steps of training.
We validate STAMP against recent advanced pruning methods on benchmark
datasets, on which it not only obtains significantly improved compression rates
over the baselines at similar accuracy, but also orders of magnitude faster
training speed.
