  The training of Generative Adversarial Networks (GANs) requires a large
amount of data, stimulating the development of new data augmentation methods to
alleviate the challenge. Oftentimes, these methods either fail to produce
enough new data or expand the dataset beyond the original knowledge domain. In
this paper, we propose a new way of representing the available knowledge in the
manifold of data barycenters. Such a representation allows performing data
augmentation based on interpolation between the nearest data elements using
Wasserstein distance. The proposed method finds cliques in the
nearest-neighbors graph and, at each sampling iteration, randomly draws one
clique to compute the Wasserstein barycenter with random uniform weights. These
barycenters then become the new natural-looking elements that one could add to
the dataset. We apply this approach to the problem of landmarks detection and
augment the available landmarks data within the dataset. Additionally, the idea
is validated on cardiac data for the task of medical segmentation. Our approach
reduces the overfitting and improves the quality metrics both beyond the
original data outcome and beyond the result obtained with classical
augmentation methods.
