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 quantification : Wu @cite_24 use the k-means clustering algorithm to accelerate and compress the convolutional layer and the fully connected layer of the model to obtain better quantization results by reducing the estimation error of the output response of each layer, and proposed an effective training scheme to suppress the multi-layer cumulative error after quantization. Jacob B @ proposed a method that quantify weights and inputs as uint8, and bias to unit32, as the same time, the forward uses quantization, and the backward correction error is not quantized to ensure that the convolutional neural networks performance and speed of inference during training.
@cite_24
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Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4 6× speed-up and 15 20× compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.