基于线性注意力机制的单样本生成对抗网络研究

目前，使用单样本训练生成对抗网络已经成为研究人员关注的重点。但是，网络模型不容易收敛，生成的图像结构易崩塌，训练速度慢等问题依旧亟待解决。研究人员提出在生成对抗网络中使用自注意力模型用以获取样本更大范围的结构，提高生成图像的质量。但是，传统的卷积自注意力模型由于注意力图谱中的信息冗余，容易造成计算资源浪费。提出了一种新的线性注意力模型，在该模型中使用了双重归一化方法来缓解注意力模型对输入特征敏感的问题，并且基于该模型搭建了一种新的单样本生成对抗网络模型。此外，模型还使用了残差网络和光谱归一化方法用于稳定训练，降低了发生崩塌的风险。实验结果表明，相较于使用已有的网络结构，该模型具有训练速度快，生成图像的分辨率高且评价指标改善明显等特点。

Research of single sample generative adversarial networksbased on attention machanism using linear layers

At present, using single-sample training to generate adversarial networks has become the focus of researchers. However, the problems that the model is not easy to converge, the generated image structure collapses, and the training speed is slow still need to be solved urgently. Researchers propose to use a self-attention model in the generative adversarial network to obtain a larger range of samples and improve the quality of the generated images. It is found that using the traditional convolutional self-attention model causes a waste of computing resources due to the redundancy of information in the attention map. A novel linear attention model is proposed, in which a double normalization method is used to alleviate the problem of the attention model being sensitive to input features, and a new single-sample generative adversarial network model is built using this model. In addition, the model uses residual network and spectral normalization methods for stable training, reducing the risk of collapse. A large number of experiments show that, compared with the existing training model, this model has the characteristics of fast training speed, high resolution of generated images, and obvious improvement of evaluation indicators.