  This paper tackles the problem of learning a finer representation than the
one provided by training labels. This enables fine-grained category retrieval
of images in a collection annotated with coarse labels only.
  Our network is learned with a nearest-neighbor classifier objective, and an
instance loss inspired by self-supervised learning. By jointly leveraging the
coarse labels and the underlying fine-grained latent space, it significantly
improves the accuracy of category-level retrieval methods.
  Our strategy outperforms all competing methods for retrieving or classifying
images at a finer granularity than that available at train time. It also
improves the accuracy for transfer learning tasks to fine-grained datasets,
thereby establishing the new state of the art on five public benchmarks, like
iNaturalist-2018.
