EGAIN：扩展的GAN反演
EGAIN: Extended GAn INversion
AI总结：
提出EGAIN架构及其同名模型egain，用于GAN反演，通过解决先前模型缺点，实现优于最先进模型的重建质量，验证了架构有效性。
摘要：
生成对抗网络（GAN）近年来取得了显著进展，生成的图像质量越来越高，以至于难以与真实图像区分。最近的GAN已被证明能够在解耦的潜在空间中编码特征，从而能够精确控制生成面部图像的各种语义属性，如姿态、光照或性别。GAN反演，即将图像投影到GAN的潜在空间中，为操纵真实人脸图像的面部语义打开了大门。这对于许多应用（如评估人脸识别系统的性能）非常有用。在这项工作中，提出了EGAIN，一种用于构建GAN反演模型的架构。该架构明确解决了先前GAN反演模型中的一些缺点。还提出了一个基于该架构的同名具体模型egain，展示了优于最先进模型的重建质量，并验证了EGAIN架构的有效性。
英文摘要：
Generative Adversarial Networks (GANs) have witnessed significant advances in recent years, generating increasingly higher quality images, which are non-distinguishable from real ones. Recent GANs have proven to encode features in a disentangled latent space, enabling precise control over various semantic attributes of the generated facial images such as pose, illumination, or gender. GAN inversion, which is projecting images into the latent space of a GAN, opens the door for the manipulation of facial semantics of real face images. This is useful for numerous applications such as evaluating the performance of face recognition systems. In this work, EGAIN, an architecture for constructing GAN inversion models, is presented. This architecture explicitly addresses some of the shortcomings in previous GAN inversion models. A specific model with the same name, egain, based on this architecture is also proposed, demonstrating superior reconstruction quality over state-of-the-art models, and illustrating the validity of the EGAIN architecture.
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