  State-of-the-art generative models (e.g. StyleGAN3 \cite{karras2021alias})
often generate photorealistic images based on vectors sampled from their latent
space. However, the ability to control the output is limited. Here we present
our novel method for latent vector shifting for controlled output image
modification utilizing semantic features of the generated images. In our
approach we use a pre-trained model of StyleGAN3 that generates images of
realistic human faces in relatively high resolution. We complement the
generative model with a convolutional neural network classifier, namely
ResNet34, trained to classify the generated images with binary facial features
from the CelebA dataset. Our latent feature shifter is a neural network model
with a task to shift the latent vectors of a generative model into a specified
feature direction. We have trained latent feature shifter for multiple facial
features, and outperformed our baseline method in the number of generated
images with the desired feature. To train our latent feature shifter neural
network, we have designed a dataset of pairs of latent vectors with and without
a certain feature. Based on the evaluation, we conclude that our latent feature
shifter approach was successful in the controlled generation of the StyleGAN3
generator.
