GIU-GANs: Global Information Utilization for Generative Adversarial Networks

• URL: http://arxiv.org/abs/2201.10471v1
• Date: Tue, 25 Jan 2022 17:17:15 GMT
• Title: GIU-GANs: Global Information Utilization for Generative Adversarial Networks
• Authors: Yongqi Tian, Xueyuan Gong, Jialin Tang, Binghua Su, Xiaoxiang Liu, Xinyuan Zhang
• Abstract summary: 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. Batch Normalization(BN) inevitably ignores the representation differences among noise sampled by the generator, and thus degrades the generated image quality.
• Score: 3.3945834638760948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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