论文标题
一种重建所有内容的模型：一种使用随机噪声的新型方式
One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN
论文作者
论文摘要
生成的对抗网络（GAN）已实现了几种图像生成和操纵任务的最先进性能。不同的作品通过将图像嵌入特定的gan体系结构中以重建原始图像，从而改善了对甘恩潜在空间的有限理解。我们提出了一种基于样式的新型自动编码器体系结构，该体系结构可以在几个数据域中重建具有很高质量的图像。我们通过独立训练编码器和解码器在不同的数据集上训练编码器和解码器，证明了先前未知的普遍性等级。此外，我们还提供了有关著名风格建筑的噪声输入的重要性和能力的新见解。我们提出的架构可以在单个GPU上每秒处理多达40张图像，该GPU比以前的方法快28倍。最后，与图像DeNoising任务的最新作品相比，我们的模型还显示出令人鼓舞的结果，尽管该任务并未明确设计。
Generative Adversarial Networks (GANs) have achieved state-of-the-art performance for several image generation and manipulation tasks. Different works have improved the limited understanding of the latent space of GANs by embedding images into specific GAN architectures to reconstruct the original images. We present a novel StyleGAN-based autoencoder architecture, which can reconstruct images with very high quality across several data domains. We demonstrate a previously unknown grade of generalizablility by training the encoder and decoder independently and on different datasets. Furthermore, we provide new insights about the significance and capabilities of noise inputs of the well-known StyleGAN architecture. Our proposed architecture can handle up to 40 images per second on a single GPU, which is approximately 28x faster than previous approaches. Finally, our model also shows promising results, when compared to the state-of-the-art on the image denoising task, although it was not explicitly designed for this task.
