  Generative Adversarial Networks (GANs) have shown satisfactory performance in
synthetic image generation by devising complex network structure and
adversarial training scheme. Even though GANs are able to synthesize realistic
images, there exists a number of generated images with defective visual
patterns which are known as artifacts. While most of the recent work tries to
fix artifact generations by perturbing latent code, few investigate internal
units of a generator to fix them. In this work, we devise a method that
automatically identifies the internal units generating various types of
artifact images. We further propose the sequential correction algorithm which
adjusts the generation flow by modifying the detected artifact units to improve
the quality of generation while preserving the original outline. Our method
outperforms the baseline method in terms of FID-score and shows satisfactory
results with human evaluation.
