  Image ordinal classification refers to predicting a discrete target value
which carries ordering correlation among image categories. The limited size of
labeled ordinal data renders modern deep learning approaches easy to overfit.
To tackle this issue, neuron dropout and data augmentation were proposed which,
however, still suffer from over-parameterization and breaking spatial
structure, respectively. To address the issues, we first propose a grid dropout
method that randomly dropout/blackout some areas of the raining image. Then we
combine the objective of predicting the blackout patches with classification to
take advantage of the spatial information. Finally we demonstrate the
effectiveness of both approaches by visualizing the Class Activation Map (CAM)
and discover that grid dropout is more aware of the whole facial areas and more
robust than neuron dropout for small training dataset. Experiments are
conducted on a challenging age estimation dataset - Adience dataset with very
competitive results compared with state-of-the-art methods.
