Learning to Inpaint by Progressively Growing the Mask Regions
This work addresses training stability issues in generative models for image inpainting, which is an incremental improvement for computer vision applications.
The paper tackles the challenge of generating correct structures and colors in image inpainting as masked regions grow large by introducing a curriculum-style training approach that progressively increases mask sizes, validated on MSCOCO and CelebA datasets with qualitative and quantitative comparisons.
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.