  Deep discriminative models (DDMs), e.g. deep regression forests and deep
decision forests, have been extensively studied recently to solve problems such
as facial age estimation, head pose estimation, etc.. Due to a shortage of
well-labeled data that does not have noise and imbalanced distribution
problems, learning DDMs is always challenging. Existing methods usually tackle
these challenges through learning more discriminative features or re-weighting
samples. We argue that learning DDMs gradually, from easy to hard, is more
reasonable, for two reasons. First, this is more consistent with the cognitive
process of human beings. Second, noisy as well as underrepresented examples can
be distinguished by virtue of previously learned knowledge. Thus, we resort to
a gradual learning strategy -- self-paced learning (SPL). Then, a natural
question arises: can SPL lead DDMs to achieve more robust and less biased
solutions? To answer this question, this paper proposes a new SPL method: easy
and underrepresented examples first, for learning DDMs. This tackles the
fundamental ranking and selection problem in SPL from a new perspective:
fairness. Our idea is fundamental and can be easily combined with a variety of
DDMs. Extensive experimental results on three computer vision tasks, i.e.,
facial age estimation, head pose estimation, and gaze estimation, show our new
method gains considerable performance improvement in both accuracy and
fairness. Source code is available at https://github.com/learninginvision/SPU.
