  Deep Metric Learning (DML) learns a non-linear semantic embedding from input
data that brings similar pairs together while keeping dissimilar data away from
each other. To this end, many different methods are proposed in the last decade
with promising results in various applications. The success of a DML algorithm
greatly depends on its loss function. However, no loss function is perfect, and
it deals only with some aspects of an optimal similarity embedding. Besides,
the generalizability of the DML on unseen categories during the test stage is
an important matter that is not considered by existing loss functions. To
address these challenges, we propose novel approaches to combine different
losses built on top of a shared deep feature extractor. The proposed ensemble
of losses enforces the deep model to extract features that are consistent with
all losses. Since the selected losses are diverse and each emphasizes different
aspects of an optimal semantic embedding, our effective combining methods yield
a considerable improvement over any individual loss and generalize well on
unseen categories. Here, there is no limitation in choosing loss functions, and
our methods can work with any set of existing ones. Besides, they can optimize
each loss function as well as its weight in an end-to-end paradigm with no need
to adjust any hyper-parameter. We evaluate our methods on some popular datasets
from the machine vision domain in conventional Zero-Shot-Learning (ZSL)
settings. The results are very encouraging and show that our methods outperform
all baseline losses by a large margin in all datasets.
