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Deep convolutional neural networks are shown to be overkill with high
parametric and computational redundancy in many application scenarios, and an
increasing number of works have explored model pruning to obtain lightweight
and efficient networks. However, most existing pruning approaches are driven by
empirical heuristic and rarely consider the joint impact of channels, leading
to unguaranteed and suboptimal performance. In this paper, we propose a novel
channel pruning method via Class-Aware Trace Ratio Optimization (CATRO) to
reduce the computational burden and accelerate the model inference. Utilizing
class information from a few samples, CATRO measures the joint impact of
multiple channels by feature space discriminations and consolidates the
layer-wise impact of preserved channels. By formulating channel pruning as a
submodular set function maximization problem, CATRO solves it efficiently via a
two-stage greedy iterative optimization procedure. More importantly, we present
theoretical justifications on convergence of CATRO and performance of pruned
networks. Experimental results demonstrate that CATRO achieves higher accuracy
with similar computation cost or lower computation cost with similar accuracy
than other state-of-the-art channel pruning algorithms. In addition, because of
its class-aware property, CATRO is suitable to prune efficient networks
adaptively for various classification subtasks, enhancing handy deployment and
usage of deep networks in real-world applications.