Title: Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks,
Abstract: While extraordinary progress has been made towards developing neural network
architectures for classification tasks, commonly used loss functions such as
the multi-category cross entropy loss are inadequate for ranking and ordinal
regression problems. To address this issue, approaches have been developed that
transform ordinal target variables series of binary classification tasks,
resulting in robust ranking algorithms with good generalization performance.
However, to model ordinal information appropriately, ideally, a rank-monotonic
prediction function is required such that confidence scores are ordered and
consistent. We propose a new framework (Consistent Rank Logits, CORAL) with
theoretical guarantees for rank-monotonicity and consistent confidence scores.
Through parameter sharing, our framework benefits from low training complexity
and can easily be implemented to extend common convolutional neural network
classifiers for ordinal regression tasks. Furthermore, our empirical results
support the proposed theory and show a substantial improvement compared to the
current state-of-the-art ordinal regression method for age prediction from face
images.