Efficient human activity classification via sparsity‐driven transfer learning

The deployment of deep neural networks in real‐world radar‐based human activity classification is largely hindered by both the high computational cost and the large amount of training samples. In this study, the authors propose a method to simultaneously reduce the computational burden and the number of labelled training samples. Different from previous transfer learning methods that simply prune fully‐connected layers and modify the weights of the convolutional layers, they enforce filter‐level sparsity in the transfer learning from ImageNet to the micro‐Doppler measurements. Through the sparsity‐driven transfer learning, unimportant convolutional filters can be identified and then be pruned. Therefore, a light but effective transfer learned net can be obtained. The experiments demonstrate the sparsity‐driven transfer learned VGG‐19 Net not only outperforms convolutional neural networks trained from scratch by nearly 10% accuracy but also gives an 11 × reduction in the number of parameters and a 10 × reduction in computing operations compared with the original VGG‐19 Net.