  While Generative Adversarial Networks (GANs) have recently found applications
in image editing, most previous GAN-based image editing methods require
largescale datasets with semantic segmentation annotations for training, only
provide high level control, or merely interpolate between different images.
Previous researchers have proposed EditGAN for high-quality, high-precision
semantic image editing with limited semantic annotations by finding `editing
vectors'. However, it is noticed that there are many features that are not
highly associated with semantics, and EditGAN may fail on them. Based on the
orthogonality of latent space observed by EditGAN, we propose a method to
estimate editing vectors that do not rely on semantic segmentation nor
differentiable feature estimation network. Our method assumes that there is a
correlation between the intensity distribution of features and the distribution
of hidden vectors, and estimates the relationship between the above
distributions by sampling the feature intensity of the image corresponding to
several hidden vectors. We modified Linear Discriminant Analysis (LDA) to deal
with both binary feature editing and continuous feature editing. We then found
that this method has a good effect in processing features such as clothing type
and texture, skin color and hair.
