  Deep metric learning has yielded impressive results in tasks such as
clustering and image retrieval by leveraging neural networks to obtain highly
discriminative feature embeddings, which can be used to group samples into
different classes. Much research has been devoted to the design of smart loss
functions or data mining strategies for training such networks. Most methods
consider only pairs or triplets of samples within a mini-batch to compute the
loss function, which is commonly based on the distance between embeddings. We
propose Group Loss, a loss function based on a differentiable label-propagation
method that enforces embedding similarity across all samples of a group while
promoting, at the same time, low-density regions amongst data points belonging
to different groups. Guided by the smoothness assumption that "similar objects
should belong to the same group", the proposed loss trains the neural network
for a classification task, enforcing a consistent labelling amongst samples
within a class. We show state-of-the-art results on clustering and image
retrieval on several datasets, and show the potential of our method when
combined with other techniques such as ensembles
