An Efficient Binary Search Based Neuron Pruning Method for ConvNet Condensation

Convolutional neural networks (CNNs) have been widely applied in the field of computer vision. Nowadays, the architecture of CNNs is becoming more and more complex, involving more layers and more neurons per layer. The augmented depth and width of CNNs will lead to greatly increased computational and memory costs, which may limit CNNs practical utility. However, as demonstrated in previous research, CNNs of complex architecture may contain considerable redundancy in terms of hidden neurons. In this work, we propose a magnitude based binary neuron pruning method which can selectively prune neurons to shrink the network size while keeping the performance of the original model without pruning. Compared to some existing neuron pruning methods, the proposed method can achieve higher compression rate while automatically determining the number of neurons to be pruned per hidden layer in an efficient way.