MicroNet: Realizing Micro Neural Network via Binarizing GhostNet
Binary Neural Networks (BNNs) are regarded as very effective approaches to reduce the high computational and memory cost of deep convolutional neural networks; however, most of the previous works only focus on the binarization of traditional large scale networks. In this paper, to further decrease the computational and memory complexity, we propose to binarize the lightweight network: GhostNet, which can generate feature maps with fewer operations. We first modify the ghost block for reducing the information loss caused by the binarization. Moreover, to enhance the performance of binarized GhostNet, some training schemes are introduced including gradient approximation and new activation layers. Various experiments on CIFAR10 dataset show that, compared with other methods, the MicroNet can achieve better performance with fewer computational and memory cost.
2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP)