论文标题
关于生成对抗网络中的噪声
On Noise Injection in Generative Adversarial Networks
论文作者
论文摘要
事实证明，注入噪声是产生高保真图像的关键技术之一。尽管它在甘恩斯成功使用了，但其有效性的机制仍然尚不清楚。在本文中，我们提出了一个几何框架，以理论上分析注射gan中的噪声的作用。基于Riemannian几何形状，我们成功地将噪声注入框架建模为地球法线坐标上的模糊等效性。在我们的理论的指导下，我们发现现有方法不完整，并设计了一种新的噪声策略。图像产生和GAN倒置的实验证明了我们方法的优越性。
Noise injection has been proved to be one of the key technique advances in generating high-fidelity images. Despite its successful usage in GANs, the mechanism of its validity is still unclear. In this paper, we propose a geometric framework to theoretically analyze the role of noise injection in GANs. Based on Riemannian geometry, we successfully model the noise injection framework as fuzzy equivalence on the geodesic normal coordinates. Guided by our theories, we find that the existing method is incomplete and a new strategy for noise injection is devised. Experiments on image generation and GAN inversion demonstrate the superiority of our method.
