[22] E. L. Denton, S. Chintala, a. szlam, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” in Advances in Neural Information Processing Systems Curran Associates, Inc., 2015, pp. 1486–1494.https://arxiv.org/abs/1506.05751 [23] P. Is...
通过结合这些措施,在几个基准数据集上将FID提高了至少 17.5%,在合成场景上获得了更显著的提升(在 FID 中高达 47.5%)。 2、Dual Projection Generative Adversarial Networks for Conditional Image Generation 条件生成对抗网络 (cGAN) 扩展了无条件 GAN ,可以从样本中学习联合数据标签分布,是能够生成高保真图像的强大生...
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, Gordon Wetzstein. CVPR 2021. [PDF] [Project] [Code]GAN Inversion MethodsThe section primarily encompasses general-purpose 2D or 3D inversion techniques...
[20] E. L. Denton, S. Chintala, a. szlam, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” in Advances in Neural Information Processing Systems Curran Associates, Inc., 2015, pp. 1486–1494. [21] H. Zhang, T. Xu, H. Li, S. Zhan...
StyleGAN:A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras 2018 paper link 1024x1024 human 4.04 指标: 1 Inception Score (IS,越大越好) IS用来衡量GAN网络的两个指标:1. 生成图片的质量 和2. 多样性 2 Fréchet Inception Distance (FID,越小越好) 在FID中我们用相同的...
11、Unsupervised Image Generation with Infifinite Generative Adversarial Networks 图像生成在计算机视觉中得到大量研究,其中一项核心挑战是无监督图像生成。生成对抗网络 (GAN) 作为一种隐式方法在这个方向上取得了巨大的成功,被广泛采用。 GAN 存在模式坍塌、非结构化潜在空间、无法计算似然等问题。本文提出一种新的无...
2023 Elsevier Inc.Time series imputation is essential for real-world applications. Though the emergence of Generative Adversarial Networks (GANs) and Graph Convolution Networks (GCNs) provides more possibilities to improve imputation performance, how to achieve the optimal latent code and precisely model...
4、Gradient Normalization for Generative Adversarial Networks 本文提出一种新的归一化方法:梯度归一化(GN),以解决生成式对抗网络(GANs)梯度不稳定问题。与现有的梯度惩罚和谱归一化等不同,本文的GN算法只对判别器函数施加梯度范数约束;在4个数据集上进行的大量实验表明,方法在Frechet Inception Distance和Inception Sco...
Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has
Chintala, a. szlam, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” in Advances in Neural Information Processing Systems Curran Associates, Inc., 2015, pp. 1486–1494.Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks...