  Deep neural advancements have recently brought remarkable image synthesis
performance to the field of image inpainting. The adaptation of generative
adversarial networks (GAN) in particular has accelerated significant progress
in high-quality image reconstruction. However, although many notable GAN-based
networks have been proposed for image inpainting, still pixel artifacts or
color inconsistency occur in synthesized images during the generation process,
which are usually called fake textures. To reduce pixel inconsistency disorder
resulted from fake textures, we introduce a GAN-based model using dynamic
attention map (DAM-GAN). Our proposed DAM-GAN concentrates on detecting fake
texture and products dynamic attention maps to diminish pixel inconsistency
from the feature maps in the generator. Evaluation results on CelebA-HQ and
Places2 datasets with other image inpainting approaches show the superiority of
our network.
