Convolutional neural networks have a significant improvement in the accuracy of Object detection. As convolutional neural networks become deeper, the accuracy of detection is also obviously improved, and more floating-point calculations are needed. Many researchers use the knowledge distillation method to improve the accuracy of student networks by transferring knowledge from a deeper and larger teachers network to a small student network, in object detection. Most methods of knowledge distillation need to designed complex cost functions and they are aimed at the two-stage object detection algorithm. This paper proposes a clean and effective knowledge distillation method for the one-stage object detection. The feature maps generated by teacher network and student network are used as true samples and fake samples respectively, and generate adversarial training for both to improve the performance of the student network in one-stage object detection.
Network Compression : many researchers believe that deep neural networks are over-parameterized, and it has too many redundant neurons and connections. He [8] thought each layer of neurons of convolutional neural networks are sparse, and they use lasso regression to find the most representative neuron per layer of convolutional neural networks reconstructs the output of this layer. Zhuang @cite_10 believe that layer-by-layer channel pruning affects the discriminating ability of Convolutional neural networks, so the auxiliary ability of convolutional neural networks is preserved by adding auxiliary loss in the fine-tune and pruning stages.
@cite_10
2963094099
Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either (i) train from scratch with sparsity constraints on channels, or (ii) minimize the reconstruction error between the pre-trained feature maps and the compressed ones. Both strategies suffer from limitations: the former kind is computationally expensive and difficult to converge, whilst the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. To overcome these drawbacks, we investigate a simple-yet-effective method, named discrimination-aware channel pruning (DCP), which seeks to select those channels that really contribute to discriminative power. To this end, we introduce additional losses into the network to increase the discriminative power of intermediate layers. We then propose to select the most discriminative channels for each layer, where both an additional loss and the reconstruction error are considered. Last, we propose a greedy algorithm to make channel selection and parameter optimization in an iterative way. Extensive experiments demonstrate the effectiveness of our method. For example, on ILSVRC-12, our pruned ResNet-50 with 30 reduction of channels even outperforms the original model by 0.39 in top-1 accuracy.