  Generative Adversarial Network (GAN) based localized image editing can suffer
from ambiguity between semantic attributes. We thus present a novel objective
function to evaluate the locality of an image edit. By introducing the
supervision from a pre-trained segmentation network and optimizing the
objective function, our framework, called Locally Effective Latent Space
Direction (LELSD), is applicable to any dataset and GAN architecture. Our
method is also computationally fast and exhibits a high extent of
disentanglement, which allows users to interactively perform a sequence of
edits on an image. Our experiments on both GAN-generated and real images
qualitatively demonstrate the high quality and advantages of our method.
