  Image inpainting is one of the most challenging tasks in computer vision.
Recently, generative-based image inpainting methods have been shown to produce
visually plausible images. However, they still have difficulties to generate
the correct structures and colors as the masked region grows large. This
drawback is due to the training stability issue of the generative models. This
work introduces a new curriculum-style training approach in the context of
image inpainting. The proposed method increases the masked region size
progressively in training time, during test time the user gives variable size
and multiple holes at arbitrary locations. Incorporating such an approach in
GANs may stabilize the training and provides better color consistencies and
captures object continuities. We validate our approach on the MSCOCO and CelebA
datasets. We report qualitative and quantitative comparisons of our training
approach in different models.
