  Person re-identification is a problem of identifying individuals across
non-overlapping cameras. Although remarkable progress has been made in the
re-identification problem, it is still a challenging problem due to appearance
variations of the same person as well as other people of similar appearance.
Some prior works solved the issues by separating features of positive samples
from features of negative ones. However, the performances of existing models
considerably depend on the characteristics and statistics of the samples used
for training. Thus, we propose a novel framework named sampling independent
robust feature representation network (SirNet) that learns disentangled feature
embedding from randomly chosen samples. A carefully designed sampling
independent maximum discrepancy loss is introduced to model samples of the same
person as a cluster. As a result, the proposed framework can generate
additional hard negatives/positives using the learned features, which results
in better discriminability from other identities. Extensive experimental
results on large-scale benchmark datasets verify that the proposed model is
more effective than prior state-of-the-art models.
