Although recent deep generative models are able to generate high-resolution, diverse natural samples from complex datasets, the generated samples still exist some problems in terms of images structure and detailed texture. In this paper, we propose a novel network architecture–SEDA-GAN that can learn the potential relationship in the dimension of the channel to enhance the generation performance of GAN. The proposed architecture applies Squeeze-and-Excitation(SE) block for feature recalibration to model channel-interdependencies within the GAN feature, and it also incorporates a dual-attention(DA) model with a channel attention mechanism in the GAN framework that can obtain global dependencies between channels. After conducting some comparative experiments on CIFAR and ImageNet datasets by using model BIGGAN as a baseline, our model performance has a certain improvement when evaluating on Fr$\acute{e}$chet Inception Distance(FID) and Inception Score(IS) respectively.

尽管最近的深度生成模型能够从复杂的数据集中生成高分辨率的各种自然样本，但是生成的样本在图像结构和详细纹理方面仍然存在一些问题。在本文中，我们提出了一种新颖的网络架构SEDA-GAN，它可以学习信道维度上的潜在关系，从而增强GAN的生成性能。拟议的体系结构应用挤压和激励（SE）块进行特征重新校准，以对GAN特征内的通道相互依赖性进行建模，并且在GAN框架中将双注意力（DA）模型与通道注意机制结合在一起，该模型可以获取通道之间的全局依赖性。在以BIGGAN模型为基准对CIFAR和ImageNet数据集进行了一些比较实验之后，$\stackrel{``}{Ë}$chet起始距离（FID）和起始分数（IS）。