  Generative models have the ability to synthesize data points drawn from the
data distribution, however, not all generated samples are high quality. In this
paper, we propose using a combination of coresets selection methods and
``entropic regularization'' to select the highest fidelity samples. We leverage
an Energy-Based Model which resembles a variational auto-encoder with an
inference and generator model for which the latent prior is complexified by an
energy-based model. In a semi-supervised learning scenario, we show that
augmenting the labeled data-set, by adding our selected subset of samples,
leads to better accuracy improvement rather than using all the synthetic
samples.
