  Metric learning has become an attractive field for research on the latest
years. Loss functions like contrastive loss, triplet loss or multi-class N-pair
loss have made possible generating models capable of tackling complex scenarios
with the presence of many classes and scarcity on the number of images per
class not only work to build classifiers, but to many other applications where
measuring similarity is the key. Deep Neural Networks trained via metric
learning also offer the possibility to solve few-shot learning problems.
Currently used state of the art loss functions such as triplet and contrastive
loss functions, still suffer from slow convergence due to the selection of
effective training samples that has been partially solved by the multi-class
N-pair loss by simultaneously adding additional samples from the different
classes. In this work, we extend triplet and multiclass-N-pair loss function by
proposing the constellation loss metric where the distances among all class
combinations are simultaneously learned. We have compared our constellation
loss for visual class embedding showing that our loss function over-performs
the other methods by obtaining more compact clusters while achieving better
classification results.
