  Probability distributions produced by the cross-entropy loss for ordinal
classification problems can possess undesired properties. We propose a
straightforward technique to constrain discrete ordinal probability
distributions to be unimodal via the use of the Poisson and binomial
probability distributions. We evaluate this approach in the context of deep
learning on two large ordinal image datasets, obtaining promising results.
