Low-Complexity Dynamic Channel Scaling of Noise-Resilient CNN for Intelligent Edge Devices

In this paper, we present a novel channel scaling scheme for convolutional neural networks (CNNs), which can improve the recognition accuracy for the practical distorted images without increasing the network complexity. During the training phase, the proposed work first prepares multiple filters under the same CNN architecture by taking account of different noise models and strengths. We then newly introduce an FFT-based noise classifier, which determines the noise property in the received input image by calculating the partial sum of the frequency-domain values. Based on the detected noise class, we dynamically change the filters of each CNN layer to provide the dedicated recognition. Furthermore, we propose a channel scaling technique to reduce the number of active filter parameters if the input data is relatively clean. Experimental results show that the proposed dynamic channel scaling reduces the computational complexity as well as the energy consumption, still providing the acceptable accuracy for intelligent edge devices.