  For deep ordinal classification, learning a well-structured feature space
specific to ordinal classification is helpful to properly capture the ordinal
nature among classes. Intuitively, when Euclidean distance metric is used, an
ideal ordinal layout in feature space would be that the sample clusters are
arranged in class order along a straight line in space. However, enforcing
samples to conform to a specific layout in the feature space is a challenging
problem. To address this problem, in this paper, we propose a novel Constrained
Proxies Learning (CPL) method, which can learn a proxy for each ordinal class
and then adjusts the global layout of classes by constraining these proxies.
Specifically, we propose two kinds of strategies: hard layout constraint and
soft layout constraint. The hard layout constraint is realized by directly
controlling the generation of proxies to force them to be placed in a strict
linear layout or semicircular layout (i.e., two instantiations of strict
ordinal layout). The soft layout constraint is realized by constraining that
the proxy layout should always produce unimodal proxy-to-proxies similarity
distribution for each proxy (i.e., to be a relaxed ordinal layout). Experiments
show that the proposed CPL method outperforms previous deep ordinal
classification methods under the same setting of feature extractor.
