The paper "Interpreting the Latent Space of GANs for Semantic Face Editing" by Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou explores the latent space of Generative Adversarial Networks (GANs) to understand how they map latent codes to photo-realistic images. The authors propose a novel framework called InterFaceGAN to interpret the latent semantics learned by GANs for semantic face editing. They find that well-trained GANs learn disentangled representations of various facial attributes through linear transformations. The framework enables precise control over facial attributes such as gender, age, expression, and the presence of eyeglasses, even for pose variations and artifact corrections. The method is further extended to real image manipulation using GAN inversion methods and encoder-involved models. Extensive experiments on PGGAN and StyleGAN demonstrate the effectiveness of InterFaceGAN in separating and manipulating different facial attributes without retraining the GAN models.The paper "Interpreting the Latent Space of GANs for Semantic Face Editing" by Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou explores the latent space of Generative Adversarial Networks (GANs) to understand how they map latent codes to photo-realistic images. The authors propose a novel framework called InterFaceGAN to interpret the latent semantics learned by GANs for semantic face editing. They find that well-trained GANs learn disentangled representations of various facial attributes through linear transformations. The framework enables precise control over facial attributes such as gender, age, expression, and the presence of eyeglasses, even for pose variations and artifact corrections. The method is further extended to real image manipulation using GAN inversion methods and encoder-involved models. Extensive experiments on PGGAN and StyleGAN demonstrate the effectiveness of InterFaceGAN in separating and manipulating different facial attributes without retraining the GAN models.