  Deep discriminative models (e.g. deep regression forests, deep neural
decision forests) have achieved remarkable success recently to solve problems
such as facial age estimation and head pose estimation. Most existing methods
pursue robust and unbiased solutions either through learning discriminative
features, or reweighting samples. We argue what is more desirable is learning
gradually to discriminate like our human beings, and hence we resort to
self-paced learning (SPL). Then, a natural question arises: can self-paced
regime lead deep discriminative models to achieve more robust and less biased
solutions? To this end, this paper proposes a new deep discriminative
model--self-paced deep regression forests with consideration on
underrepresented examples (SPUDRFs). It tackles the fundamental ranking and
selecting problem in SPL from a new perspective: fairness. This paradigm is
fundamental and could be easily combined with a variety of deep discriminative
models (DDMs). Extensive experiments on two computer vision tasks, i.e., facial
age estimation and head pose estimation, demonstrate the efficacy of SPUDRFs,
where state-of-the-art performances are achieved.
