This paper describes a simple technique to analyze Generative Adversarial
Networks (GANs) and create interpretable controls for image synthesis, such as
change of viewpoint, aging, lighting, and time of day. We identify important
latent directions based on Principal Components Analysis (PCA) applied either
in latent space or feature space. Then, we show that a large number of
interpretable controls can be defined by layer-wise perturbation along the
principal directions. Moreover, we show that BigGAN can be controlled with
layer-wise inputs in a StyleGAN-like manner. We show results on different GANs
trained on various datasets, and demonstrate good qualitative matches to edit
directions found through earlier supervised approaches.

这篇论文介绍了一种简单的技术，用于分析生成对抗网络（GANs）并创建图像合成的可解释性控制，例如视角变化、衰老、照明和白天时间，通过主成分分析（PCA）在潜在空间或特征空间中应用来识别重要的潜在方向。然后，我们展示了大量可以通过沿着主要方向进行层间扰动来定义的可解释控制。此外，我们展示了 BigGAN 可以通过类似 StyleGAN 的方式用层次输入进行控制，并显示了在不同数据集上训练的各种 GAN 上的结果，并通过较早的监督方法找到了良好的定性匹配。