  Pruning large neural networks while maintaining their performance is often
desirable due to the reduced space and time complexity. In existing methods,
pruning is done within an iterative optimization procedure with either
heuristically designed pruning schedules or additional hyperparameters,
undermining their utility. In this work, we present a new approach that prunes
a given network once at initialization prior to training. To achieve this, we
introduce a saliency criterion based on connection sensitivity that identifies
structurally important connections in the network for the given task. This
eliminates the need for both pretraining and the complex pruning schedule while
making it robust to architecture variations. After pruning, the sparse network
is trained in the standard way. Our method obtains extremely sparse networks
with virtually the same accuracy as the reference network on the MNIST,
CIFAR-10, and Tiny-ImageNet classification tasks and is broadly applicable to
various architectures including convolutional, residual and recurrent networks.
Unlike existing methods, our approach enables us to demonstrate that the
retained connections are indeed relevant to the given task.
