In recent years, with the rapid development of artificial intelligence, image generation based on deep learning has dramatically advanced. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, since convolutions are limited by spatial-agnostic and channel-specific, features extracted by traditional GANs based on convolution are constrained. Therefore, GANs are unable to capture any more details per image. On the other hand, straightforwardly stacking of convolutions causes too many parameters and layers in GANs, which will lead to a high risk of overfitting. To overcome the aforementioned limitations, in this paper, we propose a new GANs called Involution Generative Adversarial Networks (GIU-GANs). GIU-GANs leverages a brand new module called the Global Information Utilization (GIU) module, which integrates Squeeze-and-Excitation Networks (SENet) and involution to focus on global information by channel attention mechanism, leading to a higher quality of generated images. Meanwhile, Batch Normalization(BN) inevitably ignores the representation differences among noise sampled by the generator, and thus degrade the generated image quality. Thus we introduce Representative Batch Normalization(RBN) to the GANs architecture for this issue. The CIFAR-10 and CelebA datasets are employed to demonstrate the effectiveness of our proposed model. A large number of experiments prove that our model achieves state-of-the-art competitive performance.

翻译：近些年来,随着人工智能的迅速发展,基于深层学习的图像生成已大有进展。基于创世反反变网络(GANs)的图像生成是一项很有希望的研究。然而,由于演进受到空间-不可知性和频道特有的限制,传统的GANs基于变迁而绘制的特征受到限制,因此,GANs无法从每幅图像中获取更多细节。另一方面,直接堆叠的GANs的参数和层层将会导致过多的GANs,从而导致过高的超版风险。为了克服上述局限性,我们在本文件中提议了一个新的GANs,名为Incienteral Aversarial网络(GIU-GANs)。GIU-GANs利用了一个名为全球信息利用模块的品牌新模块,该模块将Squeze-Exucreview网络(SENet)和演进通过频道关注机制侧重于全球信息,从而导致生成的图像质量更高。与此同时,BAC(BN)不可避免地忽略了上述限制。GU-GAR-RA的展示质量。我们所制作的常规图像的样本展示了我们所制作的模型。