  The convolutional neural network has achieved great success in fulfilling
computer vision tasks despite large computation overhead against efficient
deployment. Structured (channel) pruning is usually applied to reduce the model
redundancy while preserving the network structure, such that the pruned network
can be easily deployed in practice. However, existing structured pruning
methods require hand-crafted rules which may lead to tremendous pruning space.
In this paper, we introduce Differentiable Annealing Indicator Search (DAIS)
that leverages the strength of neural architecture search in the channel
pruning and automatically searches for the effective pruned model with given
constraints on computation overhead. Specifically, DAIS relaxes the binarized
channel indicators to be continuous and then jointly learns both indicators and
model parameters via bi-level optimization. To bridge the non-negligible
discrepancy between the continuous model and the target binarized model, DAIS
proposes an annealing-based procedure to steer the indicator convergence
towards binarized states. Moreover, DAIS designs various regularizations based
on a priori structural knowledge to control the pruning sparsity and to improve
model performance. Experimental results show that DAIS outperforms
state-of-the-art pruning methods on CIFAR-10, CIFAR-100, and ImageNet.
