论文标题
一项有关利用预先训练的生成对抗网络进行图像编辑和修复的调查
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration
论文作者
论文摘要
由于简单但有效的训练机制和出色的图像产生质量，生成的对抗网络（GAN）引起了极大的关注。具有生成照片现实的高分辨率（例如$ 1024 \ times1024 $）的能力，最近的GAN模型大大缩小了生成的图像与真实图像之间的差距。因此，许多最近的作品表明，通过利用良好的潜在空间和学识渊博的gan先验来利用预先训练的GAN模型的新兴兴趣。在本文中，我们简要回顾了从三个方面利用预先培训的大规模GAN模型的最新进展，即1）培训大规模生成的对抗网络，2）探索和理解预先训练的GAN模型，以及3）利用这些模型来进行这些模型，以进行图像恢复和编辑。有关相关方法和存储库的更多信息，请访问https://github.com/csmliu/pretretaining-gans。
Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality. With the ability to generate photo-realistic high-resolution (e.g., $1024\times1024$) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore, many recent works show emerging interest to take advantage of pre-trained GAN models by exploiting the well-disentangled latent space and the learned GAN priors. In this paper, we briefly review recent progress on leveraging pre-trained large-scale GAN models from three aspects, i.e., 1) the training of large-scale generative adversarial networks, 2) exploring and understanding the pre-trained GAN models, and 3) leveraging these models for subsequent tasks like image restoration and editing. More information about relevant methods and repositories can be found at https://github.com/csmliu/pretrained-GANs.
