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We develop an approach to growing deep network architectures over the courseof training, driven by a principled combination of accuracy and sparsityobjectives. Unlike existing pruning or architecture search techniques thatoperate on full-sized models or supernet architectures, our method can startfrom a small, simple seed architecture and dynamically grow and prune bothlayers and filters. By combining a continuous relaxation of discrete networkstructure optimization with a scheme for sampling sparse subnetworks, weproduce compact, pruned networks, while also drastically reducing thecomputational expense of training. For example, we achieve $49.7\%$ inferenceFLOPs and $47.4\%$ training FLOPs savings compared to a baseline ResNet-50 onImageNet, while maintaining $75.2\%$ top-1 accuracy -- all without anydedicated fine-tuning stage. Experiments across CIFAR, ImageNet, PASCAL VOC,and Penn Treebank, with convolutional networks for image classification andsemantic segmentation, and recurrent networks for language modeling,demonstrate that we both train faster and produce more efficient networks thancompeting architecture pruning or search methods.

Growing Efficient Deep Networks by Structured Continuous Sparsification