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.
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