论文标题
Express Wavenet-具有随机移位小波图案的低参数光学神经网络
Express Wavenet -- a low parameter optical neural network with random shift wavelet pattern
论文作者
论文摘要
Express Wavenet是一种改进的光学衍射神经网络。在每一层，它都使用小波样图案来调节光波的相位。对于具有N2像素的输入图像，Express WaveNet将参数编号从O（N2）降低到O（N）。只需要一百分点的参数，准确性仍然很高。在MNIST数据集中，仅需要1229个参数即可获得92％的准确性，而标准光网络需要125440参数。随机移位小波更生动地显示了光网的特性。特别是在训练过程中消失的梯度现象。我们为此问题提供了修改的高速公路结构。实验验证了随机移位小波和高速公路结构的影响。我们的工作表明，与其他神经网络相比，光学衍射网络将使用的参数少得多。源代码可在https://github.com/closest-git/onnet上找到。
Express Wavenet is an improved optical diffractive neural network. At each layer, it uses wavelet-like pattern to modulate the phase of optical waves. For input image with n2 pixels, express wavenet reduce parameter number from O(n2) to O(n). Only need one percent of the parameters, and the accuracy is still very high. In the MNIST dataset, it only needs 1229 parameters to get accuracy of 92%, while the standard optical network needs 125440 parameters. The random shift wavelets show the characteristics of optical network more vividly. Especially the vanishing gradient phenomenon in the training process. We present a modified expressway structure for this problem. Experiments verified the effect of random shift wavelet and expressway structure. Our work shows optical diffractive network would use much fewer parameters than other neural networks. The source codes are available at https://github.com/closest-git/ONNet.
