  Filter pruning is widely used to reduce the computation of deep learning,
enabling the deployment of Deep Neural Networks (DNNs) in resource-limited
devices. Conventional Hard Filter Pruning (HFP) method zeroizes pruned filters
and stops updating them, thus reducing the search space of the model. On the
contrary, Soft Filter Pruning (SFP) simply zeroizes pruned filters, keeping
updating them in the following training epochs, thus maintaining the capacity
of the network. However, SFP, together with its variants, converges much slower
than HFP due to its larger search space. Our question is whether SFP-based
methods and HFP can be combined to achieve better performance and speed up
convergence. Firstly, we generalize SFP-based methods and HFP to analyze their
characteristics. Then we propose a Gradually Hard Filter Pruning (GHFP) method
to smoothly switch from SFP-based methods to HFP during training and pruning,
thus maintaining a large search space at first, gradually reducing the capacity
of the model to ensure a moderate convergence speed. Experimental results on
CIFAR-10/100 show that our method achieves the state-of-the-art performance.
