  Metric learning involves learning a discriminative representation such that
embeddings of similar classes are encouraged to be close, while embeddings of
dissimilar classes are pushed far apart. State-of-the-art methods focus mostly
on sophisticated loss functions or mining strategies. On the one hand, metric
learning losses consider two or more examples at a time. On the other hand,
modern data augmentation methods for classification consider two or more
examples at a time. The combination of the two ideas is under-studied.
  In this work, we aim to bridge this gap and improve representations using
mixup, which is a powerful data augmentation approach interpolating two or more
examples and corresponding target labels at a time. This task is challenging
because unlike classification, the loss functions used in metric learning are
not additive over examples, so the idea of interpolating target labels is not
straightforward. To the best of our knowledge, we are the first to investigate
mixing both examples and target labels for deep metric learning. We develop a
generalized formulation that encompasses existing metric learning loss
functions and modify it to accommodate for mixup, introducing Metric Mix, or
Metrix. We also introduce a new metric - utilization, to demonstrate that by
mixing examples during training, we are exploring areas of the embedding space
beyond the training classes, thereby improving representations. To validate the
effect of improved representations, we show that mixing inputs, intermediate
representations or embeddings along with target labels significantly
outperforms state-of-the-art metric learning methods on four benchmark deep
metric learning datasets.
