  Person re-identification is a challenging task because of the high
intra-class variance induced by the unrestricted nuisance factors of variations
such as pose, illumination, viewpoint, background, and sensor noise. Recent
approaches postulate that powerful architectures have the capacity to learn
feature representations invariant to nuisance factors, by training them with
losses that minimize intra-class variance and maximize inter-class separation,
without modeling nuisance factors explicitly. The dominant approaches use
either a discriminative loss with margin, like the softmax loss with the
additive angular margin, or a metric learning loss, like the triplet loss with
batch hard mining of triplets. Since the softmax imposes feature normalization,
it limits the gradient flow supervising the feature embedding. We address this
by joining the losses and leveraging the triplet loss as a proxy for the
missing gradients. We further improve invariance to nuisance factors by adding
the discriminative task of predicting attributes. Our extensive evaluation
highlights that when only a holistic representation is learned, we consistently
outperform the state-of-the-art on the three most challenging datasets. Such
representations are easier to deploy in practical systems. Finally, we found
that joining the losses removes the requirement for having a margin in the
softmax loss while increasing performance.
