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Image inpainting is a restoration method that reconstructs missing imageparts. However, a carefully selected mask of known pixels that yield a highquality inpainting can also act as a sparse image representation. Thischallenging spatial optimisation problem is essential for practicalapplications such as compression. So far, it has been almost exclusivelyadressed by model-based approaches. First attempts with neural networks seempromising, but are tailored towards specific inpainting operators or requirepostprocessing.  To address this issue, we propose the first generative adversarial network(GAN) for spatial inpainting data optimisation. In contrast to previousapproaches, it allows joint training of an inpainting generator and acorresponding mask optimisation network. With a Wasserstein distance, we ensurethat our inpainting results accurately reflect the statistics of naturalimages. This yields significant improvements in visual quality and speed overconventional stochastic models. It also outperforms current spatialoptimisation networks.

A Wasserstein GAN for Joint Learning of Inpainting and Spatial Optimisation