Automatic Channel Pruning with Hyper-parameter Search and Dynamic Masking

Modern deep neural network models tend to be large and computationally intensive. One typical solution to this issue is model pruning. However, most current model pruning algorithms depend on hand crafted rules or need to input the pruning ratio beforehand. To overcome this problem, we propose a learning based automatic channel pruning algorithm for deep neural network, which is inspired by recent automatic machine learning (Auto ML). A two objectives' pruning problem that aims for the weights and the remaining channels for each layer is first formulated. An alternative optimization approach is then proposed to derive the channel numbers and weights simultaneously. In the process of pruning, we utilize a searchable hyper-parameter, remaining ratio, to denote the number of channels in each convolution layer, and then a dynamic masking process is proposed to describe the corresponding channel evolution. To adjust the trade-off between accuracy of a model and the pruning ratio of floating point operations, a new loss function is further introduced. Extensive experimental results on benchmark datasets demonstrate that our scheme achieves competitive results for neural network pruning.