  Despite the rapid advancement of semantic discovery in the latent space of
Generative Adversarial Networks (GANs), existing approaches either are limited
to finding global attributes or rely on a number of segmentation masks to
identify local attributes. In this work, we present a highly efficient
algorithm to factorize the latent semantics learned by GANs concerning an
arbitrary image region. Concretely, we revisit the task of local manipulation
with pre-trained GANs and formulate region-based semantic discovery as a dual
optimization problem. Through an appropriately defined generalized Rayleigh
quotient, we manage to solve such a problem without any annotations or
training. Experimental results on various state-of-the-art GAN models
demonstrate the effectiveness of our approach, as well as its superiority over
prior arts regarding precise control, region robustness, speed of
implementation, and simplicity of use.
