Current deep generative adversarial networks (GANs) can synthesize high-quality (HQ) images, so learning representation with GANs is favorable. GAN inversion is one of emerging approaches that study how to invert images into latent space. Existing GAN encoders can invert images on StyleGAN, but cannot adapt to other deep GANs. We propose a novel approach to address this issue. By evaluating diverse similarity in latent vectors and images, we design an adaptive encoder, named diverse similarity encoder (DSE), that can be expanded to a variety of state-of-the-art GANs. DSE makes GANs reconstruct higher fidelity images from HQ images, no matter whether they are synthesized or real images. DSE has unified convolutional blocks and adapts well to mainstream deep GANs, e.g., PGGAN, StyleGAN, and BigGAN.

翻译：当前深度生成对抗网络（GAN）能够合成高质量（HQ）图像，因此利用GAN学习表征具有显著优势。GAN反演是研究如何将图像映射至隐空间的新兴方法之一。现有GAN编码器可在StyleGAN上实现图像反演，但无法适配其他深度GAN架构。本文提出一种创新方法以解决该问题。通过评估隐向量与图像间的多样性相似度，我们设计了一种自适应编码器——多样性相似性编码器（DSE），该编码器可扩展至多种前沿GAN模型。DSE使GAN能够从高质量图像（无论是合成图像还是真实图像）重建出更高保真度的图像。DSE采用统一卷积模块，能良好适配主流深度GAN架构，例如PGGAN、StyleGAN和BigGAN。