  Many industrial applications use Metric Learning as a way to circumvent
scalability issues when designing systems with a high number of classes.
Because of this, this field of research is attracting a lot of interest from
the academic and non-academic communities. Such industrial applications require
large-scale datasets, which are usually generated with web data and, as a
result, often contain a high number of noisy labels. While Metric Learning
systems are sensitive to noisy labels, this is usually not tackled in the
literature, that relies on manually annotated datasets.
  In this work, we propose a Metric Learning method that is able to overcome
the presence of noisy labels using our novel Smooth Proxy-Anchor Loss. We also
present an architecture that uses the aforementioned loss with a two-phase
learning procedure. First, we train a confidence module that computes sample
class confidences. Second, these confidences are used to weight the influence
of each sample for the training of the embeddings. This results in a system
that is able to provide robust sample embeddings.
  We compare the performance of the described method with current
state-of-the-art Metric Learning losses (proxy-based and pair-based), when
trained with a dataset containing noisy labels. The results showcase an
improvement of 2.63 and 3.29 in Recall@1 with respect to MultiSimilarity and
Proxy-Anchor Loss respectively, proving that our method outperforms the
state-of-the-art of Metric Learning in noisy labeling conditions.
