  Image Inpainting is a task that aims to fill in missing regions of corrupted
images with plausible contents. Recent inpainting methods have introduced
perceptual and style losses as auxiliary losses to guide the learning of
inpainting generators. Perceptual and style losses help improve the perceptual
quality of inpainted results by supervising deep features of generated regions.
However, two challenges have emerged with the usage of auxiliary losses: (i)
the time-consuming grid search is required to decide weights for perceptual and
style losses to properly perform, and (ii) loss terms with different auxiliary
abilities are equally weighted by perceptual and style losses. To meet these
two challenges, we propose a novel framework that independently weights
auxiliary loss terms and adaptively adjusts their weights within a single
training process, without a time-consuming grid search. Specifically, to
release the auxiliary potential of perceptual and style losses, we propose two
auxiliary losses, Tunable Perceptual Loss (TPL) and Tunable Style Loss (TSL) by
using different tunable weights to consider the contributions of different loss
terms. TPL and TSL are supersets of perceptual and style losses and release the
auxiliary potential of standard perceptual and style losses. We further propose
the Auxiliary Weights Adaptation (AWA) algorithm, which efficiently reweights
TPL and TSL in a single training process. AWA is based on the principle that
the best auxiliary weights would lead to the most improvement in inpainting
performance. We conduct experiments on publically available datasets and find
that our framework helps current SOTA methods achieve better results.
