  Image inpainting refers to the restoration of an image with missing regions
in a way that is not detectable by the observer. The inpainting regions can be
of any size and shape. This is an ill-posed inverse problem that does not have
a unique solution. In this work, we focus on learning-based image completion
methods for multiple and diverse inpainting which goal is to provide a set of
distinct solutions for a given damaged image. These methods capitalize on the
probabilistic nature of certain generative models to sample various solutions
that coherently restore the missing content. Along the chapter, we will analyze
the underlying theory and analyze the recent proposals for multiple inpainting.
To investigate the pros and cons of each method, we present quantitative and
qualitative comparisons, on common datasets, regarding both the quality and the
diversity of the set of inpainted solutions. Our analysis allows us to identify
the most successful generative strategies in both inpainting quality and
inpainting diversity. This task is closely related to the learning of an
accurate probability distribution of images. Depending on the dataset in use,
the challenges that entail the training of such a model will be discussed
through the analysis.
