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摘要:
当前光子神经网络的研究主要集中在单一模态网络的性能提升上，而缺少对多模态信息处理的研究。与单一模态网络相比，多模态学习可以利用不同模态信息之间的互补性，因此，多模态学习可以使得模型学习到的表示更加完备。本文提出了将光子神经网络和多模态融合技术相结合的方法。首先，利用光子卷积神经网络和光子人工神经网络相结合构建异构光子神经网络，并通过异构光子神经网络处理多模态数据。其次，在融合阶段通过引入注意力机制提升融合效果，最终提高任务分类的准确率。在多模态手写数字数据集分类任务上，使用拼接方法融合的异构光子神经网络的分类准确率为95.75%；引入注意力机制融合的异构光子神经网络的分类准确率为98.31%，并且优于当前众多先进单一模态的光子神经网络。结果显示：与电子异构神经网络相比，该模型训练速度提升了1.7倍。与单一模态的光子神经网络模型相比，异构光子神经网络可以使得模型学习到的表示更加完备，从而有效地提高多模态手写数字数据集分类的准确率。
Abstract:Current study on photonic neural networks mainly focuses on improving the performance of single-modal networks, while study on multimodal information processing is lacking. Compared with single-modal networks, multimodal learning utilizes complementary information between modalities. Therefore, multimodal learning can make the representation learned by the model more complete. In this paper, we propose a method that combines photonic neural networks and multimodal fusion techniques. First, a heterogeneous photonic neural network is constructed by combining a photonic convolutional neural network and a photonic artificial neural network, and multimodal data are processed by the heterogeneous photonic neural network. Second, the fusion performance is enhanced by introducing attention mechanism in the fusion stage. Ultimately, the accuracy of task classification is improved. In the MNIST dataset of handwritten digits classification task, the classification accuracy of the heterogeneous photonic neural network fused by the splicing method is 95.75%; the heterogeneous photonic neural network fused by introducing the attention mechanism is classified with an accuracy of 98.31%, which is better than many current advanced single-modal photonic neural networks. Compared with the electronic heterogeneous neural network, the training speed of the model is improved by 1.7 times; compared with the single-modality photonic neural network model, the heterogeneous photonic neural network can make the representation learned by the model more complete, thus effectively improving the classification accuracy of MNIST dataset of handwritten digits.
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Key words:
- photonic neural network /
- multimodal /
- attention mechanism
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图 1 异构光子神经网络的结构示意图
Figure 1. Schematic diagram of the structure of the heterogeneous photonic neural network
图 3 （a）AbsSquared非线性激活函数结构及（b）其测试结果
Figure 3. (a) AbsSquared nonlinear activation function structure and (b) the test results
图 7 基于注意力机制的异构光子神经网络结构示意图
Figure 7. Schematic diagram of heterogeneous photonic neural network structure based on attention mechanism
图 8 基于注意力机制的异构光子神经网络的学习率和优化器的选择
Figure 8. Learning rate and optimizer selection for heterogeneous photonic neural networks based on attentionmechanism
图 9 随机高斯噪声对训练集准确率的影响
Figure 9. The effect of random Gaussian noise on the accuracy of the training set
表 1 拼接融合的异构电子神经网络训练各部分时间占比
Table 1. Time share of each part of training for heterogeneous electronic neural networks with splicing and fusion
正向传播 反向传播 参数更新时间 总时间 时间/s 6.39 7.58 1.19 15.16 占比/% 42.14 50.00 7.86 100.00 
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表 2 基于注意力机制融合的异构电子神经网络训练各部分时间占比
Table 2. Time share of each part of training of heterogeneous electronic neural networks based on the fusion of attention mechanisms
正向传播 反向传播 参数更新时间 总时间 时间/s 6.53 8.18 1.09 15.80 占比/% 41.33 51.75 6.92 100.00
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