  Channel pruning has been identified as an effective approach to constructing
efficient network structures. Its typical pipeline requires iterative pruning
and fine-tuning. In this work, we propose a novel single-shot channel pruning
approach based on alternating direction methods of multipliers (ADMM), which
can eliminate the need for complex iterative pruning and fine-tuning procedure
and achieve a target compression ratio with only one run of pruning and
fine-tuning. To the best of our knowledge, this is the first study of
single-shot channel pruning. The proposed method introduces filter-level
sparsity during training and can achieve competitive performance with a simple
heuristic pruning criterion (L1-norm). Extensive evaluations have been
conducted with various widely-used benchmark architectures and image datasets
for object classification purpose. The experimental results on classification
accuracy show that the proposed method can outperform state-of-the-art network
pruning works under various scenarios.
