  Network pruning is an important research field aiming at reducing
computational costs of neural networks. Conventional approaches follow a fixed
paradigm which first trains a large and redundant network, and then determines
which units (e.g., channels) are less important and thus can be removed. In
this work, we find that pre-training an over-parameterized model is not
necessary for obtaining the target pruned structure. In fact, a fully-trained
over-parameterized model will reduce the search space for the pruned structure.
We empirically show that more diverse pruned structures can be directly pruned
from randomly initialized weights, including potential models with better
performance. Therefore, we propose a novel network pruning pipeline which
allows pruning from scratch. In the experiments for compressing classification
models on CIFAR10 and ImageNet datasets, our approach not only greatly reduces
the pre-training burden of traditional pruning methods, but also achieves
similar or even higher accuracy under the same computation budgets. Our results
facilitate the community to rethink the effectiveness of existing techniques
used for network pruning.
