StyleAutoEncoder：利用预训练StyleGAN操纵图像属性
StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN
AI总结：
本文提出轻量级插件模块StyleAutoEncoder，与预训练StyleGAN结合即可高效操纵图像属性，在效果不逊于基于可逆归一化流的最新方法的同时，实现更简单、更快速且设计更灵活。
摘要：
深度条件生成模型是创建高质量图像和编辑其属性的优秀工具。然而，从头训练现代生成模型非常昂贵，并且需要大量的计算资源。在本文中，我们引入了StyleAutoEncoder（StyleAE），这是一个轻量级的AutoEncoder模块，它作为预训练生成模型的插件工作，并允许操纵图像的请求属性。所提出的方法为在有限计算资源下训练深度生成模型提供了一种经济高效的解决方案，使其成为广泛应用中具有前景的技术。我们通过将StyleAutoEncoder与StyleGAN（目前最顶尖的生成模型之一）结合来对其进行评估。我们的实验表明，StyleAutoEncoder在操纵图像属性方面至少与基于可逆归一化流的最先进算法一样有效。然而，它更简单、更快，并且在设计神经网络时提供了更多的自由度。
英文摘要：
Deep conditional generative models are excellent tools for creating high-quality images and editing their attributes. However, training modern generative models from scratch is very expensive and requires large computational resources. In this paper, we introduce StyleAutoEncoder (StyleAE), a lightweight AutoEncoder module, which works as a plugin for pre-trained generative models and allows for manipulating the requested attributes of images. The proposed method offers a cost-effective solution for training deep generative models with limited computational resources, making it a promising technique for a wide range of applications. We evaluate StyleAutoEncoder by combining it with StyleGAN, which is currently one of the top generative models. Our experiments demonstrate that StyleAutoEncoder is at least as effective in manipulating image attributes as the state-of-the-art algorithms based on invertible normalizing flows. However, it is simpler, faster, and gives more freedom in designing neural
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
