  Recent GAN-based image inpainting approaches adopt an average strategy to
discriminate the generated image and output a scalar, which inevitably lose the
position information of visual artifacts. Moreover, the adversarial loss and
reconstruction loss (e.g., l1 loss) are combined with tradeoff weights, which
are also difficult to tune. In this paper, we propose a novel detection-based
generative framework for image inpainting, which adopts the min-max strategy in
an adversarial process. The generator follows an encoder-decoder architecture
to fill the missing regions, and the detector using weakly supervised learning
localizes the position of artifacts in a pixel-wise manner. Such position
information makes the generator pay attention to artifacts and further enhance
them. More importantly, we explicitly insert the output of the detector into
the reconstruction loss with a weighting criterion, which balances the weight
of the adversarial loss and reconstruction loss automatically rather than
manual operation. Experiments on multiple public datasets show the superior
performance of the proposed framework. The source code is available at
https://github.com/Evergrow/GDN_Inpainting.
