  Image inpainting methods have shown significant improvements by using deep
neural networks recently. However, many of these techniques often create
distorted structures or blurry textures inconsistent with surrounding areas.
The problem is rooted in the encoder layers' ineffectiveness in building a
complete and faithful embedding of the missing regions. To address this
problem, two-stage approaches deploy two separate networks for a coarse and
fine estimate of the inpainted image. Some approaches utilize handcrafted
features like edges or contours to guide the reconstruction process. These
methods suffer from huge computational overheads owing to multiple generator
networks, limited ability of handcrafted features, and sub-optimal utilization
of the information present in the ground truth. Motivated by these
observations, we propose a distillation based approach for inpainting, where we
provide direct feature level supervision for the encoder layers in an adaptive
manner. We deploy cross and self distillation techniques and discuss the need
for a dedicated completion-block in encoder to achieve the distillation target.
We conduct extensive evaluations on multiple datasets to validate our method.
