  Recent advances in deep learning have shown exciting promise in filling large
holes and lead to another orientation for image inpainting. However, existing
learning-based methods often create artifacts and fallacious textures because
of insufficient cognition understanding. Previous generative networks are
limited with single receptive type and give up pooling in consideration of
detail sharpness. Human cognition is constant regardless of the target
attribute. As multiple receptive fields improve the ability of abstract image
characterization and pooling can keep feature invariant, specifically, deep
inception learning is adopted to promote high-level feature representation and
enhance model learning capacity for local patches. Moreover, approaches for
generating diverse mask images are introduced and a random mask dataset is
created. We benchmark our methods on ImageNet, Places2 dataset, and CelebA-HQ.
Experiments for regular, irregular, and custom regions completion are all
performed and free-style image inpainting is also presented. Quantitative
comparisons with previous state-of-the-art methods show that ours obtain much
more natural image completions.
