The paper explores the following question: If an autoencoder is learned with adversarial training where the inputs to the discriminator is not the reconstruction from autoencoder but that of a reconstruction using interpolations of pairs (or more) of encodings of the training examples, would that lead to better representation learning? Results on simpler datasets showcases efficacy, while at the same time, evaluating the approach on more complex/real-world datasets would make the paper more compelling. The paper can also benefit from rigorour analysis of the Bernoulli mixup. Aside: crossover in biology happens at recombination hotspots and not at random. They are much more structured.