  In many real-world prediction tasks, class labels include information about
the relative ordering between labels, which is not captured by commonly-used
loss functions such as multi-category cross-entropy. Recently, the deep
learning community adopted ordinal regression frameworks to take such ordering
information into account. Neural networks were equipped with ordinal regression
capabilities by transforming ordinal targets into binary classification
subtasks. However, this method suffers from inconsistencies among the different
binary classifiers. To resolve these inconsistencies, we propose the COnsistent
RAnk Logits (CORAL) framework with strong theoretical guarantees for
rank-monotonicity and consistent confidence scores. Moreover, the proposed
method is architecture-agnostic and can extend arbitrary state-of-the-art deep
neural network classifiers for ordinal regression tasks. The empirical
evaluation of the proposed rank-consistent method on a range of face-image
datasets for age prediction shows a substantial reduction of the prediction
error compared to the reference ordinal regression network.
