论文标题
具有体重噪声注射训练的衍射神经网络
A Diffractive Neural Network with Weight-Noise-Injection Training
论文作者
论文摘要
我们提出了一个基于体重噪声注入训练的衍射神经网络，具有强大的鲁棒性，该网络可实现准确且基于快速的基于光学的分类，而衍射层具有一定量的表面形状误差。据我们所知，这是第一次在训练过程中使用注射体重噪声来减少外部干扰对深度学习推断结果的影响。在提出的方法中，衍射神经网络在重量噪声注入模式下学习了输入图像和标签之间的映射，使网络的重量对适度变化不敏感，这以较低的成本提高了网络的噪声阻力。通过比较不同噪声下网络的准确性，可以验证拟议的网络（SRNN）在严重的噪声下仍保持更高的精度。
We propose a diffractive neural network with strong robustness based on Weight Noise Injection training, which achieves accurate and fast optical-based classification while diffraction layers have a certain amount of surface shape error. To the best of our knowledge, it is the first time that using injection weight noise during training to reduce the impact of external interference on deep learning inference results. In the proposed method, the diffractive neural network learns the mapping between the input image and the label in Weight Noise Injection mode, making the network's weight insensitive to modest changes, which improve the network's noise resistance at a lower cost. By comparing the accuracy of the network under different noise, it is verified that the proposed network (SRNN) still maintains a higher accuracy under serious noise.
