用轻量级编码器实现预训练生成模型的属性编辑，高效低成本。
StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN
- 引入轻量级StyleAE编码器，作为插件接入预训练模型
- 在图像属性操控上达到当前顶尖方法效果，速度更快
- 适合资源受限场景，易集成于各类生成模型
深度条件生成模型是生成高质量图像和编辑图像属性的强大工具。然而，从头训练现代生成模型成本极高，需要大量计算资源。本文提出风格自编码器（StyleAutoEncoder, StyleAE），一种轻量级自编码器模块，可作为插件与预训练生成模型结合，实现对图像指定属性的操控。该方法为计算资源有限的情况下训练深度生成模型提供了经济高效的解决方案，具有广泛的应用前景。我们通过将StyleAE与当前顶级生成模型StyleGAN结合进行评估，实验表明其在图像属性操控方面至少与基于可逆归一化流的最先进算法相当，但结构更简单、运行更快，并在神经网络设计上提供更多自由度。
原文摘要 · Abstract (English)
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
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