  This paper considers learning deep features from long-tailed data. We observe
that in the deep feature space, the head classes and the tail classes present
different distribution patterns. The head classes have a relatively large
spatial span, while the tail classes have significantly small spatial span, due
to the lack of intra-class diversity. This uneven distribution between head and
tail classes distorts the overall feature space, which compromises the
discriminative ability of the learned features. Intuitively, we seek to expand
the distribution of the tail classes by transferring from the head classes, so
as to alleviate the distortion of the feature space. To this end, we propose to
construct each feature into a "feature cloud". If a sample belongs to a tail
class, the corresponding feature cloud will have relatively large distribution
range, in compensation to its lack of diversity. It allows each tail sample to
push the samples from other classes far away, recovering the intra-class
diversity of tail classes. Extensive experimental evaluations on person
re-identification and face recognition tasks confirm the effectiveness of our
method.
